Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians. Recent developments in computer operations and the rapid development of “big data” have significantly advanced artificial intelligence (AI) and machine learning (ML) technology and their applications in various fields such as medicine Figure 1). We also present the development of several models based on ML algorithms.Chronic airway diseases, such as asthma, chronic obstructive pulmonary disease (COPD) and bronchiectasis, are lifelong and life-threatening pathological conditions that extensively affect people of all ages, races, and sex worldwide In order to facilitate understanding, we quickly explain the general concepts and terminologies of AI/ML that commonly appeared in this review. In addition, we summarize the evaluation indicators and current limitations of ML.Figure 2). Supervised learning trains ML algorithms to labeled data. These labels, that include data types, data attributes and feature point locations, are used as expected effects to continuously modify the prediction results of the ML model. Common tasks for supervised learning are regression and classification for continuous and categorical outcome variables, respectively Table 1 and Figure  introduce and quickly explain common ML algorithms reviewed in this article. Based on different ML algorithms, several ML models with different functions have been developed so far. It is important to note that an optimal ML model cannot be easily developed with a limited dataset. Instead, a satisfactory ML model should be constructed in two phases: the model is developed in the training phase and then its performance is assessed in the testing phase Figure ).In general, AI refers to the technology that represents human intelligence through computer programs. ML is a branch of AI technology based on statistical techniques for self-learning and the development of problem-solving skills. In particular, ML uses complex algorithms to analyze large amounts of data, identify patterns, make predictions that do not require specific codes, and evolve with increasing sample size to improve learning. ML technology can be divided into supervised, semi-supervised, unsupervised and reinforcement learning In order to evaluate the performance of trained ML models, several reasonable evaluation indicators must be used. Generally, the ML model selects different evaluation indicators according to the different classification and regression tasks. In classification, the evaluation indicators are often accuracy, false positive rate, false negative rate, sensitivity , specificity, precision, F1-score, C-index (concordance index), receiver operating characteristic curve and the area underneath it (AUC). The regression tasks focus on the difference between the predicted and true value. Therefore, the evaluation indicators include mean square error, root mean square error, mean absolute error, and median absolute deviation.Although ML technology is continuously growing in the medical field, its application is greatly limited due to issues related to the availability of adequate data , experiments and methods, and ethics As a heterogeneous disease, asthma is often under- or overdiagnosed, especially in poor areas. In fact, almost 20-73% of cases remain undiagnosed, while about 30-35% of people diagnosed with asthma do not actually have the condition Table 2). In another cross-sectional study, an ML model based on natural language processing algorithm was also developed by mining EHRs to automatically screen pediatric patients who met the Asthma Predictive Index criteria for asthma diagnosis. A total of 427 subjects with an average age of 5.3 years were enrolled in the test phase, and the sensitivity, specificity, and positive and negative predictive values of the ML model reached 86%, 98%, 88%, and 98%, respectively To address this issue, EHRs and Predetermined Asthma Criteria were used in a retrospective birth cohort study to develop for the first time a natural language processing algorithm for pediatric asthma diagnosis with high sensitivity (97%), specificity (95%), as well as positive (90%) and negative (98%) predictive values. The test cohort of this study consisted by 497 children, among whom the asthma prevalence was 31%. The application of the same algorithm to records from 497 children  at another hospital showed similar sensitivity (92%), specificity (96%), and positive (89%) and negative (97%) predictive values, confirming the algorithm's efficiency in diagnosing pediatric asthma in an external EHR system. However, the algorithm should be further validated on an adult cohort Although spirometry and bronchial provocation tests are increasingly available, they require the full cooperation of patients and cannot confirm correct diagnosis of asthma. Therefore, the non-invasive forced oscillation technique, which does not require patient cooperation, was combined with four ML algorithms  to produce ML classifiers that serve as a useful and portable tool for diagnosing asthma airway obstruction Despite the lack of specific biomarkers for asthma, its diagnosis can be improved by combining multiple methods and clinical data. For instance, a novel AI system  was developed based on ML algorithm and several biomarkers determined from routine blood samples, such as platelet distribution width, white blood cell count, and eosinophil count. This system was trained using data from 319 asthmatic patients, then validated in 35 asthmatic patients with a classification accuracy of 94.15% A recent systematic review has also suggested that automated analysis of respiratory sounds by ML algorithms can be used for effective screening and diagnosis of respiratory diseases Given the usefulness of end-tidal capnography for disease diagnosis, a non-invasive, patient-independent method to process carbon dioxide waveform signals was developed based on the support vector machine classifier to differentiate 30 non-asthmatic and 43 asthmatic patients. The average accuracy, sensitivity, and specificity of the algorithm reached 94.52%, 97.67% and 90%, respectively, suggesting end-tidal capnography as an effective technique for asthma diagnosis Recently, several classical ML algorithms, such as logistic regression analysis, support vector machine, and deep neural network, were compared for their diagnostic ability when based only on symptoms and physical signs, or when based on the combination of symptoms, physical signs, biochemical findings, lung function tests, and the bronchial provocation tests. That study included 566 adult outpatients and indicated that the deep neural network model was more accurate than other conventional ML tools, reaching an accuracy of 98% when symptoms, physical signs and objective tests were also used Asthma is a heterogeneous disease with multiple phenotypes and endotypes that must be properly distinguished for precise prevention and personalized treatment Latent class analysis can generally fit a probabilistic model to a dataset of several variables such as asthma symptoms or allergy. Therefore, several recent studies have developed, verified, and applied ML-based latent class analysis for asthma classification, indicating its suitability for modelling data from symptomatic or asymptomatic asthma patients A predictor pursuit algorithm based on clinical treatment and outcome data was also developed to analyze phenotypes of 1688 childhood asthma patients. Four phenotypes were identified with better (P < 0.001) than traditional ML methods Precision medicine is an emerging approach of medical science for disease diagnosis and treatment, while genomics is an important manner. In recent years, genetic data have been combined with other clinical information  within different ML algorithms to determine asthma phenotypes ML algorithms have also been used to classify asthma phenotypes according to the disease severity. In particular, latent class analysis was applied to questionnaire data included demographic and clinical features to classify female asthma patients into four phenotypes  and male asthma patients into three phenotypes  Asthma exacerbation and admission have a significant impact on the life quality and mortality of patients. Artificial neural networks have been extensively used to monitor and manage asthma exacerbation and admission In recent studies, latent class analysis has been used to predict the exacerbation risk of asthma and the decline of lung function in school-age children The application of four ML algorithms  to predict severe exacerbations of asthma was recently reported based on daily monitoring data of 576 severe exacerbation events in 2,010 asthma patients. The logistic regression-based model yielded an optimal AUC of 0.85, sensitivity of 90% and specificity of 83% In order to assess the predictability of an Internet search index for asthma admission, an ML-based prediction model (XGBoost) was developed by combining search index and data, such as air pollution, weather, and previous admission events, yielding a maximum AUC of 0.832. However, the model performance should be further validated in other geographical regions Moreover, a comorbidity portfolio model was also designed based on ML algorithms to improve the prediction of asthma treatment costs over traditional approaches Successful monitoring of asthma control levels also plays a significant role in the treatment of the disease. Therefore, physicians' expertise was combined with an ensemble ML algorithm to detect asthma control. The optimal accuracy of the model was 91.66% and, although the study included relatively few factors affecting asthma control, the model could help clinicians develop timely treatment plans Despite the wide variety of studies on AI/ML implementation in asthma, very few studies have reported the application of AI/ML systems to the treatment of the disease, as such treatment is usually controlled by specific guidelines. In addition to the two aforementioned studies With the continuous improvement of computer learning and the accumulation of asthma-related data, the application of AI/ML in asthma has made great progress with good results for specific clinical research purposes The application of AI/ML in asthma is also still limited. For example, although the current studies can be combined with different data or variables to build ML models for identifying asthma phenotypes, the integration of relative comprehensive data or variables, including demographic, environmental, medical history, symptoms, laboratory examination, pulmonary function testing, genomics, metabolomic, and imaging, is still absent. Moreover, AI/ML methods have rarely been used to identify or predict patients with severe asthma. Such studies would be of great clinical significance, because an in-depth understanding of asthma phenotypes and the reliable identification of specific subgroups could guide the use of specific drugs, such as biological-targeted agents, to bring precision medicine to asthma patients. In addition, most studies on the application of AI/ML in asthma are based on populations and datasets from developed countries, while very little research has been conducted on populations and data in developing countries, where the need for diagnosis, treatment and management is more urgent due to the weaker healthcare systems. To make the situation more challenging, the COVID-19 pandemic has severely disrupted health services for patients with chronic airway diseases. Therefore, the use of AI/ML tools to establish a management system for such patients during an infectious disease epidemic should be seriously considered.There are no specific symptoms related to COPD, but the disease can be diagnosed using the pulmonary function test. However, its accuracy is highly dependent on the patient's cooperation, which explains the common under- and overdiagnosis of COPD in clinical practice Table 3). Despite the remarkable diagnostic accuracy of each subgroup (65%), further studies are required to improve the model performance in distinguishing COPD patients from smokers without COPD. Given the lack of specific biomarkers for COPD diagnosis, support vector machine was also integrated with two blood biomarkers, N-acetyl-glycoprotein and lipoprotein, which were obtained by comparing 54 COPD patients with 74 normal individuals. The model achieved a diagnostic accuracy of 84.62% and an AUC of 0.90 To reduce the dependence on lung function tests for early diagnosis of COPD, ML has also been used to mine and analyze transcriptomic data extracted from human bronchial epithelial cells, leading to the identification of abnormal expression of 15 genes in the disease, 10 of which had not previously been reported as COPD biomarkers. The different gene combinations were then analyzed by the random forest algorithm to distinguish non-smokers from smokers and COPD patients Respiratory sounds are an important sign of the lungs and their analysis can be useful in the diagnosis of respiratory diseases Inequities in access to medical resources also affect the diagnosis of COPD, especially in less developed areas. Therefore, an automated telehealth AI system was recently developed and verified in 780 patients from several medical institutions Some patients may also have both asthma and COPD, known as asthma-COPD overlap (ACO). However, the lack of accurate diagnostic criteria has led to insufficient data on the prevalence and treatment of ACO According to the Global Obstructive Lung Disease Initiative, COPD patients are classified into four phenotypes based on their symptomatic assessment, exacerbation and hospitalization history The assessment of persistent airflow limitation in COPD patients depends on lung function tests. However, only some COPD patients complete these tests in clinical practice, limiting the diagnosis of airway limitation to 56% Chest computed tomography has also been widely used to detect lung texture abnormalities and assess the state of COPD. However, a large amount of image data cannot be identified with the naked eye, highlighting the need for AI/ML systems in this field Assessing the severity of hospitalized acute exacerbations of COPD (AECOPD) patients is also beneficial to clinical practice. Hence, a modified decision tree algorithm was used to analyze 28 clinical features, including demographics, medical history, and biomarkers derived from 202 inpatients with severe AECOPD and 208 inpatients with mild AECOPD. The classification of severe and mild patients was based on their admission to the intensive care unit. The overall accuracy of the developed classifier reached 80.3%, suggesting that it can be used to assess the severity of hospitalized AECOPD patients Persistent chronic airway inflammation and airflow limitation in COPD can induce the recurrence of acute exacerbation and readmission. In order to effectively manage COPD patients and monitor the disease, several studies have used ML-based approaches, which proved to be more effective than conventional methods Persistent airflow limitation along with persistent respiratory symptoms make COPD a lifelong and life-threatening disease. Thus, it is particularly important to monitor variation in lung function and prevent persistent airflow limitation. A ML model based on random forest was recently developed using spirometry data obtained from 4,167 participants in order to predict individuals most likely to develop or have COPD. The primary outcome of the model was FEV1, while the secondary outcome was the risk of airflow limitation . This model may be a useful tool for personalized risk prediction of airflow limitation and early prevention of COPD Given the irreversibility of COPD, its early detection and diagnosis are crucial. Thus, six ML models were used to predict the development of COPD based on 101 single-nucleotide polymorphisms and 5 clinical characteristics of 441 patients and 192 normal participants. Among them, 9 single-nucleotide polymorphisms were significantly associated with this disease, including 6 risk and 3 protective factors. In the test set, among the examined models, the k-nearest neighbor classifier and logistic regression showed the highest precision of 82% and accuracy of 81%, while the highest sensitivity  of 100% was achieved using the multilayer perceptron classifier based on the artificial neural network algorithm ML algorithms were also used to analyze functional respiratory imaging for the prediction of exacerbation and early identification of AECOPD patients The global economic burden of COPD increases every year, a trend exacerbated by the aging population. In Europe, the total cost of COPD is estimated at 56% of annual healthcare expenditure for respiratory diseases Since COPD is one leading causes of death worldwide, some studies have also used AI/ML technology to predict the risk of mortality in patients with COPD AI/ML technologies can monitor, integrate, and analyze large-scale, heterogeneous clinical information from COPD patients; suggest optimal individualized treatments; and reduce over- or undertreatment caused by clinician errors. However, similar to asthma, we found only one study related to the application of AI/ML in COPD treatment. In that work, several ML models  were developed based on 153 predictive factors derived from telemonitoring of physiological, symptom, and baseline data from 135 patients with moderate to severe COPD. The data included demography, severity, quality of life and hospital admissions, and the goal was to detect acute exacerbations and guide the corticosteroid therapy of COPD. Irrespective of acute exacerbations or corticosteroid use, the optimal ML model  showed better AUC values than non-ML methods (0.74-0.77 vs 0.60-0.66), and its performance was not improved by adding weather data Given the structured data from pulmonary function testing and their important role in the diagnosis and management of COPD, AI/ML techniques were combined with such testing early in the field in order to help clinicians assess lung function Although there have been significant advances in understanding and managing COPD using AI/ML methods, there are still many unmet needs. For example, airflow limitation is the hallmark of COPD, and studies have found that even in patients with similar FEV1 levels, there are significant differences in the degree of respiratory symptoms, frequency of acute exacerbations, and activity endurance AI/ML may also play a significant role in the treatment and management of patients with stable chronic airway diseases. Inhalation therapy is the basic therapeutic approach for stable COPD. However, stable COPD patients may misuse inhaled devices due to the diversity of inhaled drugs and devices and the lack of medical assistance. Therefore, the application of AI/ML methods for the early detection of errors and the improvement of patient compliance should be further investigated. AI/ML studies are also worth conducting in low-income individuals or regions, as they usually exhibit high morbidity and experience a heavy financial burden due to COPD; in addition, the relatively delayed generation of clinical data in their health systems leads to poor data integrity.In recent years, the application of AI/ML techniques in the medical field has evolved rapidly, while several AI/ML tools have been extensively studied and applied to chronic airway diseases. This review summarizes the recent advances in the implementation of AI/ML techniques in the screening and diagnosis, classification and assessment, management and monitoring, as well as treatment of asthma and COPD. Our research supports that AI/ML techniques can be used effectively to analyze and integrate large, heterogeneous medical data, thus assisting physicians in making decisions and guiding clinical practice. In addition, these techniques can be applied to analyze different responses to treatment, providing therapeutic guidance for specific phenotypes required in precision medicine, and establish a management system for chronic airway disease patients during an infectious disease epidemic. However, the results about AI/ML tools should be interpreted and generalized with caution. The AI/ML techniques cannot yet replace clinicians in the diagnosis and treatment of chronic airway diseases, and further studies are needed to examine the effect of several parameters on ML model construction and to verify existing findings with larger samples and external data sources.


Activists in the global South have been navigating two powerful trends since the mid-1990s: intensifying state repression and rising investment in extractive projects from the emerging economies of Brazil, Russia, India, China, and South Africa (BRICS). In this context, this article explores the underlying forces determining the formation, endurance, and power of BRICS–South transnational advocacy networks (TANs) opposed to BRICS-based corporate extraction in the global South. By analyzing activism against Chinese, Indian, and Brazilian extractive projects in Ecuador, Ethiopia, and Mozambique, respectively, the research reveals the critical importance of domestic politics and civil society characteristics in both the BRICS and host states for shaping BRICS–South TANs, including which groups assume leadership, the extent of cross-national cooperation, and the role of nonprofits headquartered in the global North. The findings uncover core reasons for the variable resiliency and capacity of BRICS–South TANs, opening up new avenues of research and offering valuable insights for activists and policymakers. Intensifying suppression of dissent and the growing power of emerging economies over the past few decades have significantly altered the conditions and strategies of activists campaigning against transnational corporate mining, plantations, and land grabbing in the global South. Since the mid-1990s, and especially since the terrorist attacks of September 11, 2001, governments—from mature democracies to authoritarian regimes—have sought to constrain anti-government protests and nongovernmental organizations (NGOs) . Dupuy e1 BRICS–South agreements to facilitate extractive projects in South states have proliferated too, often under conditions that disadvantage the latter  and raise alarms of subimperialism and neocolonialism  and non-BRICS states of the global South  have been steadily rising .1 BRICS–onialism .2 When nregions” , p. 445.4 Caught at the intersection of these trends, how are NGOs and activists in BRICS and South states navigating their respective repressive settings to mobilize, separately or jointly, against BRICS-financed and -managed extractive projects in the global South? What role do NGOs headquartered in the global North assume in burgeoning BRICS–South alliances? Employing case studies of activism against Chinese, Indian, and Brazilian extractive projects in Ecuador, Ethiopia, and Mozambique, respectively, this article explores these questions.The growth in BRICS extractive projects in South states has created a need for stronger BRICS–South transnational advocacy networks . Yet, at the same time, activists in both emerging and developing economies are campaigning in increasingly repressive political settings and facing grave dangers from state security forces and private corporations, especially those opposing large-scale extractive projects .4 CaughtThe mobilization patterns of BRICS–South TANs, as this article shows, reflect the conditions and characteristics of civil society in both the BRICS and host state. In saying this, we are not assuming civil society is fully autonomous from the state and market. Nor are we associating civil society exclusively with democracy and individual rights as this can exclude influential forms of associational life in the global South, such as traditional authorities , religious groups, ethnic-based associations, and community self-help groups . To captFour major factors, we argue, influence how ready, willing, and able civil society in the global South is to pursue BRICS–South transnational activism: the extent of legal protections and political repression within each state, the degree of domestic political and economic stability, the experience of NGOs and Indigenous groups in resisting extractive projects at home and abroad, and the priority that local NGOs and Indigenous peoples place on building South–South solidarity. When conditions are favorable in the South state, but not the BRICS state, activism tends to be led by and take place in the South state (as in the case of China–Ecuador). In the reverse scenario, a substantial amount of the advocacy tends to take place in the BRICS or internationally (as in the case of India–Ethiopia). When conditions are reasonably favorable in both countries, civil society organizations tend to deploy strategies independently and jointly in the BRICS and South context, with support from international and North-based NGOs (as in the case of Brazil–Mozambique).Drawing on scholarship, reports, and other documentation, this article adds another layer of understanding to the literature analyzing BRICS–South TANs e.g., . This inWe begin our analysis with a brief review of the literature on South–South TANs, noting the relative lack of research on BRICS–South TANs. Next, to add new layers of understanding of the characteristics and influence of BRICS–South TANs, we investigate advocacy networks opposing BRICS extractive projects across three cases: China–Ecuador, India–Ethiopia, and Brazil–Mozambique. We conclude by comparing our case study findings and reflecting on their implications.Transnational advocacy networks comprise “actors working internationally on an issue, who are bound together by shared values, a common discourse and dense exchanges of information and services” , p. 89. Throwing boomerangs to seek overseas allies can be highly effective although, as many scholars warn, vigilance is necessary to avoid overemphasizing the influence of transnational alliances and underestimating the importance of domestic activism and political conditions . AdditioPolitics within TANs profoundly shape mobilization patterns, too. Founding members of TANs or its more powerful members , for instance, can exercise disproportionate influence on a network’s agenda and focus . This isThe scholarship on TANs reveals unique challenges to the formation and maintenance of BRICS/South–South TANs. The tendency for South investments to flow through national development banks (NDBs), rather than multilateral development banks, limits the access points for these advocacy networks . In the In response, BRICS-based civil society actors have organized counter-summits to BRICS and NDB forums . Still, Adapting to these challenges, BRICS–South TANs have found some success. The Amazonian Hydroelectric Collective anti-dam campaign, for example, successfully influenced the Peruvian state and Brazilian corporations to cancel a series of hydroelectric projects planned in Peru . FinanceScholarship on BRICS–South TANs, as this short overview suggests, is only just starting to emerge. Significant gaps remain in the scholarly knowledge of the formation, durability, extent of cross-national cooperation, role of North-based NGOs, and overall influence of these advocacy networks. The rest of this article builds on this early scholarship to deepen the understanding of the underlying forces shaping BRICS–South TANs campaigning against BRICS-based extractive projects in the global South. We begin with a look at campaigns to oppose Chinese extractive projects in Ecuador. As we will see next, Ecuadorian civil society organizations are leading resistance efforts with some support from NGOs in Latin America and the global North. Meanwhile, a China–Ecuador advocacy network has not formed and civil society ties between China and Ecuador remain weak.President Rafael Correa’s decision to nationalize Ecuador’s natural resources following his 2007 election and to default on Ecuador’s foreign debt in 2008 led several oil companies to exit the country, putting Correa in need of revenue to fund his commitments to social investments and maintain his legitimacy . China sThis influx of Chinese investments and companies has threatened communities, and civil society resistance has been fierce, particularly against large-scale mining projects . To make way for the projects, the Ecuadorian government and private security have forcibly displaced and burned down villages . IndicatEcuadorian civil society, and in particular Indigenous groups, has a long history of activism against state threats to Indigenous autonomy  and extractive projects . Thus, Ecuador’s civil society, albeit not without fractures , is relaStill, Ecuadorian civil society is navigating a context of increasing state repression. Presidents and government officials commonly seek to weaken civil society by framing environmental organizations and activists as vectors of foreign influence . The EcuWhile these assaults occurred under the tenure of President Correa (2007–2017), the trend has continued under President Lenín Moreno. In 2019, the Moreno government imposed a 2-month state of emergency to suppress protests following the adoption of austerity measures and a pro-extractivism agenda. In 2020, the government restricted the right to assemble, further threatening those resisting foreign mining on Indigenous land . The MorThe growing role of China in extractivism has posed difficult challenges for Ecuadorian activists. Chinese firms, lenders, and policy advisors tend to form close ties with the central government and largely avoid engaging with communities . State–cAt the same time, China remains largely inaccessible to the boomerang strategy. The reason is straightforward. Although Chinese civil society organizations are exerting some influence over environmental management inside China , China d5 While the Chinese government has released guidelines for overseas conduct of its companies, they are entirely voluntary and largely unenforced . They have showcased biodiversity loss and reported on the socioenvironmental impacts of Chinese extractive projects . And theLeveraging the 2008 constitutional right to FPIC, Indigenous communities have also gone to court to claim the government violated the rights of nature as well as failed to protect their constitutional right to FPIC before awarding mining concessions to Chinese firms . The couIn addition to these domestic approaches, Ecuadorian NGOs have aligned with other Latin American NGOs to raise international awareness. This includes documenting the human rights violations of Chinese extractive projects for consideration for China’s Universal Periodic Review of the United Nations Human Rights Council . NongoveOf course, lobbying and court filings in the global North do not necessarily mean North-based firms will perform any better when mining, logging, or drilling for oil in the global South. Showcasing this activism in the global North is only meant to highlight how Ecuadorian environmental defenders lose this avenue of resistance with China’s entry into overseas extraction. In the case of Chinese-based companies, as this section shows, Ecuadorian civil society has been unable to access Chinese courts or government agencies. Nor has Chinese civil society, whether for lack of interest or capacity, widely campaigned in support of Ecuadorian campaigns against Chinese extractive projects.Instead, Ecuadorian civil society has done the “heavy lifting” in resisting Chinese-based firms in Ecuador with modest backing from Latin American and North-based NGOs. In contrast, as we’ll see next, Ethiopian civil society has struggled to sustain campaigns against Indian-based extractive projects in Ethiopia, relying primarily on TANs to pressure firms, and routinely tossing boomerangs to seek support from international, North-based, and Indian-based civil society organizations.Between the mid-1990s and 2011, and especially from 2008 to 2011, the Ethiopian government leased an estimated 3–3.5 million hectares of land to foreign investors based mainly in emerging and developing economies . This laTwo Indian companies, Overseas Infrastructure Alliance and Uttam Sucrotech, won contracts for projects funded by this line of credit, illustrating the importance of the Export-Import Bank in helping Indian companies access Ethiopia . At the Ethiopian civil society flourished from the mid-1990s to mid-2000s, illustrated in part by the expansion from 70 local NGOs in 1994 to 2,275 in 2009 . ConditiBy 2011, more than 500 Ethiopian NGOs had shut down in response to this crackdown . Many ofThese repressive laws have facilitated land grabbing of Indigenous territory, particularly impacting Indigenous people  of the Gambella region and Lower Omo Valley. The villagization program has been one of the government’s instruments to enact this violence. This program has forcibly moved Indigenous peoples living on so-called “underutilized” land into villages, often with substandard medical, educational, and other services. Without consent, the government has then leased their land for plantations .These land grabs have adversely impacted between 200,000 and 500,000 people . DescribPrime Minister Abiy Ahmed’s appointment in 2018 eased the constraints on civil society somewhat, although repressive laws and state violence targeting activists and Indigenous groups opposing state-backed extractive projects endure . For exaOverall, the relatively weak position of Ethiopian rights-oriented civil society and limited legal protections for Indigenous peoples put activists campaigning against land grabbing in the precarious position of facing human rights abuses with few options for recourse.Compared to China, India is a more accessible target for the boomerang strategy. Nonetheless, in recent years, Indian civil society has been under increasing restraints as the Indian government favors service NGOs and frames rights-based NGOs as disloyal and “antinational,” especially those with foreign ties . To hostDespite these barriers, Indian civil society is in the early stages of developing an activist network that targets Indian corporations overseas . Aiding In 2013, the US-based Oakland Institute facilitated a meeting in India between these Indian activists and Indigenous Ethiopians impacted by Indian land grabbing in Ethiopia . FollowiTo try to shape India’s overseas development policies, other Indian civil society organizations have participated in initiatives such as the Forum on Indian Development Cooperation . In anotIn short, while Indian civil society faces barriers to politically organizing and shaping India’s development policies, the space permitted to organize and the experience in resisting domestic land grabs give them considerable potential to develop advocacy networks with civil society organizations in countries affected by Indian extractive companies and investment.On the other hand, state repression of NGOs and Indigenous groups in Ethiopia severely limits the domestic strategies available to resist Indian investments . Aside fForeign NGOs have been crucial for filling the advocacy vacuum created by Ethiopia’s repressive setting. Besides organizing meetings between Indian organizations and Indigenous Ethiopian activists, international NGOs such as the Oakland Institute and GRAIN have helped raise awareness by researching the human rights violations associated with extractive projects in Ethiopia . North-b6North-based academics, such as Concerned Scholars for Ethiopia, have also spoken out against the Ethiopian government in solidarity with Indigenous communities impacted by state violence associated with extractive projects . While tCivil society organizations from the global North and India, then, are in relatively prominent positions in the TANs opposing Indian extractive projects in Ethiopia as Ethiopian activists struggle to survive within a highly repressive society. The nature of TANs opposing Brazilian extractive projects in Mozambique, as the next section discusses, is different again. Here, backed by an array of North-based NGOs, civil society organizations from both countries have been participating actively in these networks, although rising state repression and political crises in both countries have been weakening the power of these advocacy networks in recent years.Brazil’s participation in South–South development increased significantly during President Luiz Inácio “Lula” da Silva’s tenure from the beginning of 2003 to the end of 2010 . The govTwo extractive projects are particularly notable in Brazilian–Mozambican relations: the Vale mining project and the ProSAVANA agricultural project. In 2004, with diplomatic assistance from the Brazilian government, the Brazilian mining giant Vale won a bid for the Mozambican Moatize coal reserves . OperatiThe ProSAVANA agricultural project, a joint collaboration between the Brazilian, Japanese, and Mozambican governments and private sector, commenced in 2009. The project was modeled after the Japanese–Brazilian PROCEDER project, a commodity export-oriented agribusiness program implemented in Brazil’s Cerrado region during the 1970s. Like Vale’s activities and the PROCEDER project, ProSAVANA forcibly displaced communities, increasing food insecurity, among other social ills .NGOs in Mozambique began to expand following the 1992 Peace Accords ending the Mozambican civil war, but they still navigate a repressive political setting. The government has a history of only supporting nonthreatening NGOs, such as service providers, and legitimizing decisions with superficial participatory processes . OrganizConsequently, civil society organizations in Mozambique tend to align themselves with state policy or adapt to the political constraints and, only rarely, openly resist the government . High prResisting large-scale extractive projects in Mozambique has become especially dangerous since foreign investment began booming a decade ago . Yet, atBrazil is a largely accessible boomerang target. For one, Brazil has a vibrant civil society with a long history of mobilizing against extractive projects within the country and alongside the civil societies in Latin America more broadly . Thus, iAlthough open to participating in South–South TANs, Brazil’s civil society has been under duress in recent years. The 2002 election of President Lula of the Partido dos Trabalhadores (Workers’ Party) brought cautious hope of greater citizen participation in decision making and a reduction of inequities . The LulThe Lula administration did make strides in formalizing civil society participation in public policy discussions at the federal level. The extractive-based development strategy, however, has created tension with (and among) segments of Brazilian civil society concerned with protecting Indigenous peoples and the environment. That tension played out, for example, when the Brazilian government ignored the concerns of some civil society organizations regarding the social and environmental impacts of the Belo Monte dam .President Jair Bolsonaro, who came to power in 2019, has been openly hostile to civil society organizations, especially environmental NGOs . At the Critics argue that Brazil’s framework for development cooperation requires stronger regulatory guidelines, greater transparency, and further clarity on the distribution of responsibilities . These fIn brief, although Brazil lacks extraterritorial legal obligations and its civil society is navigating a context of increasing state repression and resource extraction, the enthusiasm of NGOs to build transnational linkages and their experience resisting domestic extractive projects make the country a relatively accessible target for building BRICS–South TANs.For Brazilian NGOs attentive to international solidarity and shared struggle of marginalized peoples, Brazil’s foreign investment in Mozambique created an opportunity to mobilize transnationally with Mozambican and North-based civil societies . They diPrior to Vale’s expansion into Mozambique, Brazilian civil society had long resisted the company’s domestic extractive activities. In 2009, for instance, protests against Vale’s mining operation in Canada prompted the formation of the International Articulation of those Affected by Vale (AV), which mapped out Vale’s global activities and brought together activists and organizations concerned with Vale’s conduct . ThroughAV’s resistance to Vale, however, slowed dramatically after 2015 as the political conditions in Brazil and Mozambique became increasingly hostile and unfavorable . In BrazAnti-ProSAVANA activism began around 2012 when Mozambican organizations became aware of the agreement signed two years earlier without the consent of affected communities. With the support of international NGOs, Mozambican groups connected with Brazilian NGOs through participation in AV, the Via Campesina food sovereignty movement, and Rio+20 People’s Summit. In the name of both international solidarity and a common struggle (because the project could trigger an exodus of Brazilian farmers and firms to Mozambique that would compromise food security in Brazil), Brazilian NGOs worked to unite relevant groups to fight against Brazil’s agribusiness activities in Mozambique .Mozambique’s National Union of Peasants also publicly opposed the ProSAVANA project and, after learning that ProSAVANA was similar to the PROCEDER project, an Oxfam-funded civil society delegation from Mozambique and Brazil traveled to the Cerrado region of Brazil to survey the detrimental health and environmental impacts of PROCEDER. Upon returning to Mozambique, with a film crew documenting the trip, a domestic coalition authored an open letter demanding greater transparency, accountability, and respect for the autonomy of rural farmers .No to ProSAVANA campaign, which was promoted by their counterparts in Brazil and international NGOs. Parallel to these efforts, Mozambican civil society groups with a less critical stance against ProSAVANA used Brazil’s public commitment to fight hunger to lobby for social protections and consideration of family farming traditions within the project  NGOs or, to a growing degree, in the BRICS context. In this case, the BRICS–South TAN is strained, but emerging.The breadth of Brazilian and Mozambican civil society strategies to resist Vale’s coal mines and the ProSAVANA project in Mozambique stands in contrast to the largely one-sided mobilizations that have taken place to resist China’s extraction in Ecuador and India’s extraction in Ethiopia. This Brazil–Mozambique case exemplifies that an active and strong BRICS–South TAN would seem to require reasonably favorable conditions in both domestic contexts. Under such conditions, members of the Brazil-Mozambique TANs have been able to develop dense, diverse, and durable ties as well as establish consistent channels of communication, which are all features of influential TANs . AnotherFor their part, NGOs based in the global North would seem to adjust their role depending on the particular strengths and gaps of the BRICS–South TANs. This may entail, among other activities, connecting global South NGOs in coalitions, researching and publishing reports, and facilitating and financing BRICS-based or South-based advocacy strategies. In addition, North-based organizations may be able to assist BRICS civil society organizations wanting to develop outward-facing advocacy networks, including connecting them with relevant South-based NGOs. Even without the assistance of North-based NGOs, however, a BRICS–South advocacy network can still mobilize against extractive projects when conditions in both civil societies favorably align.In sum, our analysis highlights the crucial importance of local politics and the nature of civil society for shaping the formation, resiliency, and influence of BRICS–South advocacy networks that are opposing BRICS extractive projects in the global South. As the campaigns against BRICS extractive projects in Ecuador, Ethiopia, and Mozambique demonstrate, local characteristics shape the extent of cross-national cooperation, the role of North-based nonprofits, and the leadership positions of local groups within an advocacy network. Within each state, critical local characteristics include the degree of political repression and legal protections for NGOs, the extent of socioeconomic and political stability, the experience of NGOs and Indigenous groups in resisting large-scale corporate projects, and the value that local NGOs place on building South–South solidarity. These conclusions are not meant to close the book on this subject. The consequences of local conditions for transnational advocacy vary considerably, and our hope is future researchers will build on our findings to investigate additional factors that might be influencing the power and reach of BRICS–South advocacy networks, such as differing mobilization opportunities and constraints across extractive industries.


However, this association was attenuated when considering socio-demographics, smoking and cardiovascular risk factors and medication.•Our findings highlight the potential risk for an accelerated cognitive decline in this group of individuals, specially for those with bipolar disorders, and the need to further investigate the role of potential shared mechanisms. We aimed to compare trajectories of cognitive performance in individuals diagnosed with dementia with and without severe mental illness (SMI).Retrospective cohort study.We used data from a large longitudinal mental healthcare case register, the Clinical Record Interactive Search (CRIS), at the South London and Maudsley NHS Foundation Trust (SLaM) which provides mental health services to four south London boroughs.Our sample (N = 4718) consisted of any individual who had a primary or secondary diagnosis of dementia from 2007 to 2018, was 50 years old or over at first diagnosis of dementia and had at least 3 recorded Mini-Mental State Examination (MMSE) scores.Cognitive performance was measured using MMSE. Linear mixed models were fitted to explore whether MMSE trajectories differed between individuals with or without prior/current SMI diagnoses. Models were adjusted by socio-demographics, cardiovascular risk, smoking, and medication.Our results showed differences in the rate of change, where individuals with comorbid SMI had a faster decline when compared with those that have dementia without comorbid SMI. However, this association was partially attenuated when adjusted by socio-demographics, smoking and cardiovascular risk factors; and more substantially attenuated when medication was included in models. Additional analyses showed that this accelerated decline might be more evident in individuals with bipolar disorders. Future research to detangle the potential biological underlying mechanisms of these associations is needed. This is because individuals with comorbidities in general, and mental health illness in particular, are traditionally excluded from large population studies or their numbers are very small. In recent decades, there has been a growing interest to include these under-represented subpopulations. As a result, most studies have explored cognitive performance in individuals diagnosed with SMI but not dementia. Most of these studies have explored cross-sectional differences between individuals with bipolar disorders and schizophrenia,To date, most longitudinal studies have not found differences between cognitive trajectories in individuals with schizophrenia and bipolar disorders.,,,It has been suggested that the short term cognitive impairments associated with acute exacerbations of mental disorders do not result in an accelerated cognitive decline,,,Given this context, there is an ongoing debate on the nature of the progression of cognitive changes in schizophrenia and bipolar disorders and it is yet not clear whether these are associated with a neurodevelopmental or neurodegenerative process such as dementia. Understanding the underlying biological mechanisms between SMI such as schizophrenia or bipolar disorders and dementia could contribute to improve healthcare and reveal potential new therapeutic targets. Therefore, there is a need for research to disentangle the complex inter-relationships between SMI and dementia, especially given that individuals with this comorbidity pattern are at increased risk of psychiatric hospitalizations and higher levels of health care utilizationhttps://www.maudsleybrc.nihr.ac.uk/facilities/clinical-record-interactive-search-cris/cris-natural-language-processing/). The sample (N = 4718) consisted of any individual who had a primary or secondary diagnosis of dementia from 2007 to 2018, 50 years old or over at first diagnosis of dementia and at least three recorded MMSE scores, following Singer and WilletData were extracted from the South London and Maudsley NHS Foundation Trust (SLaM) Clinical Record Interactive Search (CRIS). SLAM provides mental health services to a defined geographic catchment area of four south London boroughs , with populations comparable overall with those of London as a whole in terms of age, gender, education and socio-economic status distributions.Cognitive performance. Cognitive performance was measured using extracted scores from the Mini-Mental State Examination (MMSE).Communities and Local Government, 2015). IMD scores were then categorized as deciles with lower deciles were indicative of higher deprivation. Smoking status extracted using an NLP algorithmCovariates. Socio-demographic covariates included age at baseline , sex, education, ethnicity, marital status, deprivation index and English as first language. Education was derived from records as a binary variable with two levels, primary or lower education and secondary or higher education , and extracted using NLP algorithms.Medication was extracted using a combination of structured and NLP derived data,Preliminary analyses were performed to describe the characteristics of the sample and compare these between individuals diagnosed with SMI or not. To examine group differences in cognitive trajectories and investigate associations with covariates, we used linear mixed models with random coefficients. The main advantage of using linear mixed models is that they allow us to account for between and within individual variability.Missing values in some of our covariates was larger than 30% , thus our models included a unknown category for these variables. Sensitivity analyses were performed to compare these models with cases with complete data for all covariates . In addition, further sensitivity analyses were performed to ensure the robustness of our results considering attrition due to death or potential variance heterogeneity associated to age at diagnoses. In order to examine whether there could be any potential bias associated to the premature mortality in individuals with SMI we replicated our analyses restricting the sample to the individuals who were still alive at 2019 (n = 1766) and compared the results.2 (4) = 127.31; p <0.001], not married , have higher education , be more socially deprived , at higher CVD risk , currently smoking  and taking antipsychotics  and antidepressants  (and less likely to be taking dementia-treatment agents ) when compared with those without comorbid SMI at first MMSE recorded. Within the group of individuals with dementia and SMI (2 (4) = 63.52; p <0.001], single or separated , have lower levels of education , higher levels of social deprivation  and less likely to be taking antidepressant medication  compared to those with bipolar disorders. There were no significant differences for gender, first language, smoking, CVD risk and other medication intake. Fisher exact Chi-square tests were additionally performed to explore the potential association between CVD risk and antipsychotic and no significant associations were found .As can be seen in  and SMI , we examWhen we investigated MMSE trajectories for this sample, we found that linear models showed a better fit than quadratic models (linear BIC 107342 versus quadratic BIC: 108301). The ICC for the unconditional model was 0.75, indicating significant between- and within-individual variability. At intercept level , we founWhen we further investigated whether different SMI diagnosis (schizophrenia and bipolar disorders) showed different cognitive trajectories when compared with individuals with only dementia , we founWhen sensitivity analyses included age at diagnoses similar trends were found. When we compared results with only complete cases for all covariates (n = 1453) and only those individuals that were still alive in 2019 (n = 1766) we found similar trends as slope estimates were reduced but in the same direction for fully adjusted models even considering the different length of follow up between both groups  compared to those that had only dementia .This study examined trajectories of cognitive performance in individuals diagnosed with dementia, comparing those with and without a comorbid SMI diagnosis. This study used data from a large longitudinal mental healthcare case register, and considered sociodemographic factors, health behaviors, cardiovascular risk factors, and medication as covariates.Our primary finding was that individuals with dementia with SMI diagnoses had a faster cognitive decline when compared with those without comorbid SMI. However, this association was partially explained by socio-demographic, health behaviors and CVD risk factors; and more substantially attenuated when medication was considered. Associations of socio-demographic and health-related factors with cognitive trajectories are broadly consistent with previous research in British ageing cohorts.,When we explored whether there were differences between those individuals with schizophrenia and bipolar disorders compared to those with dementia without comorbid SMI, we found that there those with dementia with comorbid SMI had higher MMSE scores at intercept level, even after adjusting for age. However, no differences were found between those individuals with schizophrenia and bipolar disorders. Although our findings are not directly comparable with previous research in individuals without dementia, they are consistent with previous cross-sectional studies which found similar profiles.With regards to medication, adjustment for dementia, antipsychotic, and antidepressant medications strongly impacted the association of interest even after accounting for the other covariates considered such as CVD risk. When we examined the role of each medication independently, we found that this impact was clear for dementia and antidepressants, the estimates suggest that there could be a greater and potentially protective impact for antipsychotic medication in the case of bipolar disorders.,Besides the strengths of our study associated with the large sample from a traditionally underrepresented population in cognitive decline studies and the richness of the longitudinal nature of the data, some limitations should be acknowledged. First, data on health behaviors was limited to smoking status, and we could not include important potential predictors such as alcohol consumption or physical activity in our models. Future studies are therefore needed to understand further the role of lifestyle and health when understanding cognitive decline in individuals with these comorbidities. Second, dementia was ascertained using routine clinical diagnoses rather than research instruments and we did not explore differences between diagnostic subgroups due to their limited sample sizes and the difficulty to distinguish those clinical diagnoses that are often recorded to facilitate and speed the patient´s journey through the different healthcare pathways. Third, there could be some potential selection bias associated to the fact that: our sample was limited to those with at least three MMSE scores available , dementia can go undiagnosed in SMI and our population has an increased risk of premature mortality.To conclude, our findings showed that individuals with comorbid SMI show a faster cognitive decline when compared with those that have dementia without comorbid SMI. This accelerated decline seems to be more evident in individuals with bipolar disorders when compared with individuals with dementia only and it is only partially explained by relevant confounders. Further research is needed to detangle the biological explanatory pathways. Clinical and public health interventions should monitor medication regimes and promote healthy lifestyles in individuals diagnosed with dementia and SMI to reduce the potential risk of accelerated cognitive decline in this population.RB conceived the idea, designed the study and the analytical plan. AM extracted, preprocessed, and analyzed the data. RS provided clinical input and all authors contributed to the interpretation of the results. RB drafted the manuscript and all authors made substantial contributions, revised this work critically and approved final version to be published.https://mrc.ukri.org) and by grant IS-BRC-1215-20018 for the National Institute for Health Research  Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. AM is funded by Takeda California, Inc. RD, RS, AR are part-funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. RD is supported by: (1) NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London, London, U.K. (2) Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust. (3) The BigData@Heart Consortijum, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement No. 116074. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA; it is chaired by DE Grobbee and SD Anker, partnering with 20 academic and industry partners and ESC. (4) The National Institute for Health Research University College London Hospitals Biomedical Research Centre. (5) National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. (5) The UK Research and Innovation London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare. (6) National Institute for Health Research (NIHR) Applied Research Collaboration South London (NIHR ARC South London) at King's College Hospital NHS Foundation Trust.RB is funded in part by grant MR/R016372/1 for the King's College London MRC Skills Development Fellowship programme funded by the UK Medical Research Council  Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.This paper represents independent research part-funded by the The authors report no conflicts with any product mentioned or concept discussed in this article.


Meaningful engagement has important implications for quality of care for persons living with dementia. Yet, little research has focused on direct care workers’ (DCWs) role in facilitating engagement opportunities for residents in assisted living. Using data from our ongoing NIA-funded study, “Meaningful Engagement and Quality of Life among Assisted Living Residents with Dementia,” we describe DCW approaches to engaging residents and the factors that influence the use and successful application of these approaches. Focal residents (N=33) were followed at four diverse assisted living communities for one year. Data includes care partner interviews (N=100), including 28 DCWs and 1560 hours of field observation data. DCWs interviewed had between 2 months and 12 years’ experience in their current position and were mostly African American and/or immigrant women of color. Findings suggest that DCW-resident interactions are key opportunities to engage residents in a meaningful way and can facilitate positive, trust-based relationships. This analysis elaborates on our previous work identifying four approaches: knowing the person, connecting with and meeting people where they are, being in the moment, and viewing all encounters as opportunities. We identified factors affecting opportunities for, and experiences with, meaningful engagement between residents and DCWs, including community staffing, consistent assignment, scheduling, DCW training and tenure, and community resources. We conclude with implications for practice emphasizing how elaboration of these approaches can inform the development of DCW training, opportunities for career advancement, and integration of approaches consistently into daily practice in an effort to support meaningful engagement of residents living with dementia.


This past year, the lives of vulnerable older adults, including those within the older LGBTQ+ community have been disrupted dramatically, as has the research agendas designed to improve their lives. Older people, including LGBTQ+ older adults with dementia, have been placed at increased risk for social isolation and mental health issues during COVID, making viable interventions even more crucial. Additionally, how research is conducted within these communities needed to be adjusted in order to preserve viability. This symposium draws upon data from the National Health Aging and Sexuality/Gender study, the first longitudinal study of LGBTQ+ older adults in the United States, as well as data from Aging with Pride: IDEA (Innovations in Dementia Empowerment and Action), the first randomized controlled trial (RCT) designed to improve quality of life of LGBTQ+ adults living with dementia and their care partners. (1) Kim and Fredriksen Goldsen examine modifiable behavioral and social factors that can improve quality of life among LGBTQ+ older adults with cognitive impairment. (2) Fredriksen Goldsen, Teri, Emlet and colleagues present initial efficacy findings from the IDEA study and how the intervention needed to be altered to be viable in a COVID world. (3) The importance of Motivational Interviewing (MI) as part of a LGBTQ+ sensitive intervention designed for LGBTQ+ older adults with dementia and their care partners is discussed by Petros, Fredriksen Goldsen and Teri. As COVID continues to impact vulnerable populations as well as research and service delivery, identifying new and innovative strategies will become increasingly important.


The study of the statistics of new drug registrations over time shows an interesting pattern. At the end of the last century, productivity, measured in terms of market launches, was declining and charts as the one shown in What might be the reason for this potentially positive trend of increasing numbers of NMEs and Biologics approvals over the last ten years?It is obvious that massive efforts in strategy adjustments, early ADMET optimization and the introduction of new assays, methods and computational techniques by pharmaceutical R & D and academia have had an effect. New disease-relevant targets have been identified and potential improvements in regulatory processes could have been beneficial. Moreover, the introduction of new therapeutic modalities, besides the so-called classical Rule of 5 (Ro5)  complianin vivo effect. Although general strategies to design oral bioavailable bRo5 drug candidates have not yet been reported, conformational flexibility has been identified as an important parameter to describe the ADME profile of large and flexible derivatives. For example, it has been recently shown that flexibility descriptors helped to improve computational solubility prediction of bRo5 compounds [Antibodies, antibody-drug conjugates, fusion proteins, neurotoxins, peptides, polymers, small interfering RNA, vaccines, etc., are reaching the market. Furthermore, the introduction of the so-called “beyond rule of five compounds” (bRo5) indicates that even for some demanding targets larger compounds (MW >> 500) can be designed and optimized to have adequate ADMET profiles and resulting bioavailability with desired ompounds . Moreoveompounds ,10.Certainly, small molecules still make up the majority of new drug market introductions, and their urgent need in the treatment of newly discovered diseases and the improvement of current therapies is impressively demonstrated during the severe ongoing Covid 19 pandemic.This special issue of ADMET and DMPK describes new therapeutic modalities, innovative compound characterization tools for drug safety evaluation/optimization and newly developed computational prediction tools for estimating drug brain uptake as examples of how the drug discovery and research fields are continuously innovating and adapting to the changing needs.In a first impressive work, experts in the field of degraders, i.e., bifunctional compounds that promote degradation of target molecules instead of inhibiting them, also called PROTACs (PROteolysis TArgeting Chimeras), have analyzed degrader data in terms of target distribution . MoreoveColleagues from the Russian Academy of Sciences in Saratov describe how Selenium NanoParticles (SeNPs) can be used to increase the bioavailability of silymarin , a drug In addition to new therapeutic principles, innovative new methods for testing and optimizing drug safety are urgently needed. In a fascinating mini-review , a jointFinally, a team of experts from the Universities of Naples and Edinburgh describes the development of in silico prediction models based on Immobilized Artificial Membrane (IAM) chromatography measurements and predicted drug properties to estimate the blood-brain barrier passage of drugs . AlthougOverall, this special issue of ADMET and DMPK provides interesting insights into selected innovative areas of drug discovery and development.


The burden of non-communicable diseases (NCDs), including cancer, in Africa is rising. Policymakers are charged with formulating evidence-based cancer control plans; however, there is a paucity of data on cancers generated from within Africa. As part of efforts to enhance cancer research training in East Africa, we performed a needs assessment and gap analysis of cancer-related research training resources in Tanzania.A mixed-methods study to evaluate existing individual, institutional, and national resources supporting cancer research training in Tanzania was conducted. Qualitative data were collected using in-depth interviews while quantitative data were collected using self-administered questionnaires and online surveys. The study also included a desk-review of policy and guidelines related to NCD research and training. Study participants were selected to represent five groups: (i) policymakers; (ii) established researchers; (iii) research support personnel; (iv) faculty members in degree training programs; and (v) post-graduate trainees.Our results identified challenges in four thematic areas. First, there is a need for coordination and monitoring of the cancer research agenda at the national level. Second, both faculty and trainees identified the need for incorporation of rigorous training to improve research competencies. Third, sustained mentoring and institutional investment in development of mentorship resources is critical to empowering early career investigators. Finally, academic institutions can enhance research outputs by providing adequate research infrastructure, prioritizing protected time for research, and recognizing research accomplishments by trainees and faculty.As we look towards establishment of cancer research training programs in East Africa, investment in the development of rigorous research training, mentorship resources, and research infrastructure will be critical to empowering local health professionals to engage in cancer research activities. Instead, many national cancer control plans are formulated based on data from high-income countries, which may not be pertinent in the African context.5Non-communicable diseases (NCDs) pose increasing challenges to health systems throughout sub-Saharan Africa. It is projected that by 2030, 1.27 million new cancer cases and 0.97 million cancer-related deaths per year will occur in Africa.6 Data indicate that less than two percent of African research scientists have received two or more grants within 15 years after training.7 This is exemplified by disproportionately low research outputs and under-representation of African researchers as first and senior authors in research conducted in Africa.9Over the past two decades, institutions in sub-Saharan Africa have made remarkable progress in building human research capacity. However, few African scientists who have achieved master’s or doctorate-level training are competitive for external funding sources.10 Cancer research in East Africa is largely nascent. While higher learning institutions in Africa offer research training, there are notable deficiencies in research competencies among graduate clinicians in areas such as clinical research methods, cancer epidemiology, biostatistics, bioinformatics, and database management.11 As part of our ongoing effort to develop a sustainable cancer research training program in Tanzania, we performed a needs assessment and gap analysis of cancer-related research training resources.Most of the research output in Africa has focused on infectious diseases, with little attention to NCDs.12 The country’s health agenda is determined by the Ministry of Health Community Development Gender Elderly and Children (Mo-HCDGEC), which is responsible for establishing policies to guide preventive, clinical, and medical research practice for both communicable diseases and NCDs. The National Institute for Medical Research (NIMR) is responsible for setting and evaluating the national research agenda, and to monitor, control, and coordinate all medical research carried out within Tanzania. Muhimbili University of Health and Allied Sciences (MUHAS) is one of the oldest medical training and research institutions in East Africa and has trained over 75% of existing researchers and healthcare providers in Tanzania; it is currently the only university in Tanzania which provides formal training in clinical oncology. It also provides training for students from throughout eastern and central Africa. Ocean Road Cancer Institute (ORCI) is the nation’s specialized cancer hospital, providing the vast majority of chemotherapy and radiotherapy care to cancer patients in Tanzania. Muhimbili National Hospital (MNH) is the national referral hospital which provides a majority of pathological, radiological, surgical oncology, and pediatric oncology services in the country. Both ORCI and MNH are teaching hospitals affiliated with MUHAS.Tanzania is a lower middle income country in East Africa with a population of 78 million people.We conducted a mixed-methods study between March and June 2019 to evaluate individual, institutional, and national resources for cancer research training in Tanzania. Qualitative data were collected using in-depth interviews (IDIs) while self-administered questionnaires and online surveys were used to collect quantitative data. The study also included a desk-review of policy and guidelines related to NCD research and training at both national and institutional levels.Study participants were purposefully selected to represent five groups uniquely positioned to respond to needs assessment regarding cancer research.Policymakers were asked to participate in IDIs. These policymakers included representatives from the NCD section and the Department of Policy and Planning at the MoHCDGEC and representatives from research units or directorates at NIMR, MUHAS, ORCI, and MNH responsible for the development and evaluation of institutional research agendas.Established researchers from MUHAS, ORCI, and MNH with expertise in cancer research were selected to participate in IDIs to assess research capacity, needs, and prioritization.Research support personnel, responsible for library services, information and communication, procurement, grant management, ethical reviews, and financial management at NIMR, MUHAS, ORCI, and MNH, were asked to participate in IDIs to assess existing research support and infrastructure.Faculty members in training degree programs with components of training focused on research methodologies and/or cancer-related specialties were asked to complete self-administered questionnaires.Post-graduate trainees enrolled in master’s programs in epidemiology, public health, or a clinical master’s program in a cancer-related specialty  were recruited to participate in an online survey.IDIs, self-administered questionnaires, and online surveys were used to collect data for this study.14Survey tools were developed from previously validated tools including a previous survey tool used by researchers behind this manuscript. Following a systematic development process, the survey tools were pilot tested prior to data collection. Different qualitative data collection methods were utilized and involved different groups of participants with the aim of collecting wider views on the different themes and draw harmonized conclusions/opinions/suggestions.20The tools aimed to collect data on four major thematic areas: (a) policies and research agendas; (b) research capacity and training needs; (c) mentorship resources; and (d) the research support environment.The IDI guides for policymakers were comprised of 18 open-ended questions focused on identifying national and institutional research needs, existing research strategies, and training. The IDI guides for established researchers were comprised of 23 open-ended questions that focused on the facilitators and barriers for career development in cancer research and included an inquiry into prior training, perceived competencies needed to conduct research, and the role of mentorship in research. The IDI guides for research support personnel were comprised of 30 open-ended questions that focused on the roles, current capacity, unmet demands for research and regulatory support, and future needs to support the development of these offices. Interviews for ethical review members focused on the frequency of the meetings, turnaround time, barriers faced, and suggestions for improvement.The IDIs were conducted by five Tanzanian team members , who had participated in the development of the interview guides. All the interviews were audio-recorded. IDIs were conducted in either English or Swahili, and conducted either in-person or via teleconference, depending on the preference of the participant.Qualitative data were transcribed verbatim and translated into English, as needed then manually analysed by theme. Textual data were independently coded and analysed by three master’s degree graduates who were independent coders who had not participated in tools development or data collection to reduce risk of opinion bias. The coding system and analytic framework were based on both a priori concepts taken from the interview guides and key themes that emerged during manual open coding process. Findings and interpretations were iteratively compared among coders and reviewed by senior researcher (EJM) for harmonization. To validate the analysis, findings were presented and discussed with the whole study team and lastly shared by our collaborators from the ministry, ORCI and MUHAS prior to manuscript finalization.The semi-structured questionnaire for faculty members had a total of 30 questions focused on self-evaluation of their professional development, competencies, personal research activities, and assessments of curricula and mentorship programs at MUHAS. The questionnaire aimed to understand experiences, knowledge, and attitudes towards cancer research and training, perceived barriers to conducting cancer research, and the adequacy of the existing training programs.For the post-graduate trainees, the web-based questionnaire consisted of 46 open-ended questions that utilized different Likert scales to assess knowledge level. Questionnaire data were collected in REDCap™, with automatically generated email reminders sent to trainees at 7-day intervals to increase response rates. Descriptive analyses were performed using SPSS version 20. The results were organized according to four previously defined themes.To evaluate existing national cancer control policies, research agendas, and research support, a review of NCD and cancer-related policies obtained from the MoHCDGEC and research agendas from NIMR, MUHAS, ORCI, and MNH was performed by three team members .All individuals who participated in IDIs provided verbal consent to participate and for audio recording. All faculty participants gave verbal consent prior to being issued a questionnaire. Consent language was included in the invitational email for the web-based questionnaires, and consent was assumed by participants selecting the link to proceed to the questionnaire data that was collected and managed using REDCap™ electronic data capture tools hosted at [San Francisco Coordinating Center (SFCC)]. The study was considered exempt from review by the MUHAS Institutional Review Board (IRB).A total of 14 IDIs, 16 self-administered questionnaires, and 60 web-based questionnaires were completed with response rates of 88%, 53%, and 48%, respectively. The characteristics of the participants are summarized in Perceived reasons for the presence of limited research on NCD and cancer in Tanzania, included: (i) a scarcity of researchers in NCD and cancer; (ii) limited interest in research due to competing needs for financial resources; (iii) lack of research competencies; (iv) limited time for research due to teaching, administrative, and clinical care responsibilities; (v) absence of guidelines for protected time for research; and (vi) inadequate remuneration for research. Among post-graduate trainees, the top three barriers to research were: (i) inadequate funding (75%); (ii) inadequate research training and skills (62%); and (iii) inadequate protected time for research (53%) . Challen21 Health Sector Strategic Plan (HSSP IV) 2015-2020,22 Strategic and Action plan for Prevention and Control of Non Communicable Diseases in Tanzania (2016-2020),23 National Cancer Control Strategy (2013-2022),24 and institutional research agendas. These documents present research policies with priorities for the country and national institutions. We noted NCDs, such as cardiovascular diseases, diabetes mellitus, chronic respiratory conditions, and cancer were consistently among the top ten priority research areas in the national research agenda.The relevant national plans that support NCD and cancer research in Tanzania include: Research Priorities for Tanzania (COSTECH) (2015-2020),Policymakers, in particular, highlighted key priorities for NCD research in Tanzania . They poRespondents also highlighted the need to develop research monitoring framework and put in place mechanisms that promote the use of local research results in informing policies. The proposed research monitoring framework should aim at identification and coordination of research work among cancer researchers in the country to avoid duplication and promote collaboration among institutions and individual researchers.Established researchers proposed a staff appraisal and promotion system that incorporates not only publications but also grant funding. They also highlighted the need for institutions to build opportunities for publication, dissemination of locally conducted research. In effort to address differences in capabilities and resources of institutions within Tanzania, they recommended development of guidelines for institutional sharing of resources.Established cancer researchers proposed priority cancer research topics across the continuum of cancer care in Tanzania and highlighted key competencies needed in the country to be able to conduct research. The specific competencies mentioned included research conceptualization, proposal development, and grant management.With regards to competencies in teaching, only 25% (n=4) of faculty respondents ranked themselves as experts in teaching research, though a majority 93.8% (n=15) had participated in research development courses as learners. Self-identified strengths among faculty members included understanding of subject matter (93%), teaching large classes (62.5%), and development of assessment methods (62.5%). Meanwhile, use of audiovisual aids in teaching, information and computer technology skills, proposal development, data analysis and manuscript writing were identified as competencies that require more training support.A majority (95%) of post-graduate trainees reported interest in incorporating research into their future careers. Despite the enthusiasm, (73%) also indicated that the existing master’s degree curriculums do not provide adequate training in research. In self-assessment of their own research knowledge and skills, most of the trainees self-reported only basic knowledge in research. About half of the respondents indicated that the current curriculum in their training course needs revision to be satisfactory in increasing competency required for conducting research . SpecifiWhen asked regarding preferred training modalities, a majority of faculty expressed preferences for short term fellowships (37.5%) and short-term trainings (50%) which provide practical skills and also on-the-job mentorship (62.5%). Long-term didactic training was only desired by a smaller proportion of participants (31.3%). Established cancer researchers further suggested the need for sustained training approaches that are tailored for clinicians and adapted to competency improvement.Established researchers consistently cited that strong mentorship has played an important role in achieving success as researchers. A majority of faculty members (75%) reported having mentors; however, they also reported that they had not received any training on mentorship principles (62.5%). All (100%) expressed the need for such training. Receipt of mentorship from experienced researchers was also consistently identified as important in training junior researchers. Of the trainees surveyed, 56% agreed with the statement that “Mentorship is important in research career development.” Despite these needs, half of the trainees (58%) either were undecided or disagreed with the statement “Getting mentors in your research topic of interest is easy.” Guidelines for mentorship exist at MUHAS, but not at MNH or ORCI. Of note, mentor-mentee pairings are typically made by administrative assignment and rarely form organically. Moreover, the intensity of the mentoring relationship relies heavily on the initiative of the mentee, and relationships are commonly perceived as unbalanced and uni-directional in terms of benefits. Finally, the interviews emphasized the overwhelming shortages in faculty with available bandwidth to provide mentorship. These shortages were attributed to lack of protected time for mentorship and competing clinical and academic responsibilities.Participants provided suggestions of what is needed for an effective mentorship program. It was proposed for the institution to develop and disseminate mentorship guidelines that exist within a framework with measurable outcomes. It was also suggested that mentorship pairings could potentially improve if they are mutually driven, self-selected by the trainee-mentee pair, and grounded in shared interests between mentor and mentee. To improve capacity for mentoring, based on the results of this study, assigning protected time, and provision of incentives either financial compensation or institutional recognition (eg certificates or awards) were envisaged to have potential to build enthusiasm and morale.All institutions included in this assessment provide some administrative support for research. The supportive services offered by these offices range from before making a grant application to after one wins a grant award. The major challenges faced by the research support offices across all institutions were lack of adequate personnel within the units and limited information and communications technology resources to support researchers .The majority of faculty and researchers expressed needs for more support in procurement, financial management, and grant management. Research support personnel interviewed reported that there is a need for institutions to develop research support and resource guidelines that can be shared by all researchers. They also recommended inclusion of teaching on budgeting and financial management for researchers in the existing master programmes curriculum. This recommendation is based on the experience of the research support office supporting researchers in developing credible research budgets and financial management plans during project implementation.26The three institutions  have IRBs with mechanisms in place to grant permission for studies to be conducted in the institution’s premises. All research in the country requires an institutional ethical review and clearance. For those research grants which involve foreign investigators approval by the National Health Research Ethics Committee (NatHREC) is required. The main purpose of NatHREC approval is to protect and manage risk to human participants involved in research, ensuring ethical practice, prevent local data and specimen exploitation, and keep record of shared materials by different collaborating bodies.The MUHAS IRB supports ORCI and MNH and other domestic institutions with interest in medical-related research in Tanzania. The committee undertakes ethical and scientific review of all proposals. The average submission volume per month was about 50 research protocols, with an expected turnaround time of three weeks for expedited review and three months for a normal review. Quality of the submitted protocol was identified as another determinant of turnaround time. Furthermore, expedited vs. normal timeframes for review are associated with different costs to the investigator(s), and fees vary depending on whether the applicant is a student, local investigator, or an international collaborator.Despite recent efforts to improve IRB functionalities to support research, several areas for improvement were identified, including: (i) a need for increased human resources to appropriately handle submission volumes; (ii) training researchers on proposal writing to improve the quality of submissions; (iii) disseminating guidelines for IRB proposal writing; (iv) developing a framework for post-award monitoring thus able to track study accruals, ethical compliance, renewals, and expirations; (v) electronic tracking of proposals; and (vi) updating IRB guidelines and standard operating procedures.Established researchers also emphasized on the need for inter-institutional collaborations, so as to promote sharing of existing novel technologies that can be utilized in cancer research looking at etiological screening, diagnostics and treatment.Our study presents results from key cancer research stakeholders and describes the policy environment, researchers’ competencies, training gaps, and research support environment in Tanzania. The achieved response rates for in-person IDI was 88%, self-administered questionnaires, 53%, and for web-based questionnaires 48%. Response rates in research are better when person-person data collection methods are employed and relatively lower in self-administered or web-based approaches. This study achieved an excellent response rates for in-person interviews and although lower, our response rates from self-administered and web-based approach included about half of the participants, relatively better than other comparable studies. Weekly follow up reminders were issued in person for faculty and via email to trainees. These reminders were repeated three times before the participants were considered non-response. We however acknowledge the potential limitation and highlights challenge researchers face in response and attrition rate in research conduct.Overall, the enthusiasm for cancer research is high; however, stakeholders unequivocally reported needs for training to build local capacity to effectively inform the revision and implementation of Tanzania’s national cancer control plan. Interviewed cancer researchers highlighted priority research areas of relevance along the cancer control continuum. However, limited research infrastructure, inadequate funding, lack of conducive environment for research, and a lack of national research coordination were identified as ongoing challenges. With each identified challenge, there is an opportunity for improvement and thus potential for improving future research development 27 the policy does not prioritize cancer research and does not provide a budget to facilitate research implementation. Moreover, the lack of a specific coordinating units for NCD research and management at the national level was highlighted by policymakers within the MoHCDGEC. Established researchers reported a growing need for coordination and monitoring of cancer research both at national and institutional levels in an effort to harmonize research efforts and to advocate for cancer research funding. Similar findings have been reported in other African countries, showcasing that locally funded training and research are critical in addressing issues in Africa’s research outputs.29NCD policies and strategies currently exist at the national level and at the national specialized hospitals and academic institutions in Tanzania. All reviewed policies primary focus on the need for human resources required to support prevention and clinical care activities for NCDs. While the National Cancer Control Strategy incorporates strategic objectives such as scaling up cancer surveillance, registries, cancer management, and prevention,31 and result in the inability to train graduates that can conduct high-quality research. Post-graduate trainees expressed high motivation to incorporate research in their careers, although existing curriculums in terminal clinical degree programs do not emphasize research training. These findings echo our previous findings from an evaluation of oncology trainees in Tanzania,11 demonstrating that this disconnect prevails across post-graduate training programs. Lack of training in research skills has been reported to hinder clinicians’ involvement in research in both developed and developing countries,34 highlighting the need for dedicated research training programs.33 Proposed strategies to improve research capacity building among early career investigators included opportunities for tailored skill-specific short-term trainings (50%) and mentorship arrangements (62%).Deficiencies in research training were consistently reported by both faculty and trainees. These self-reported deficiencies are similar to findings reported by other African universities36 Experienced researchers interviewed in this study emphasized the role that mentorship has played in their careers and the need for sustained mentorship.Evidence from research and training institutions in Africa indicates strong mentorship is associated with increased research productivity.37 in some academic settings, are not defined in the Tanzanian context. Blended roles are often attached to the single “Advisor” which is assigned by administration. In addition, there is a need for institutions to invest in development of mentorship resources, revision of existing guidelines to be more comprehensive with framework and outcome measures, faculty training on mentorship principles, and promotion of peer-to-peer mentorship among trainees.39Mentorship guidelines at MUHAS did not provide a clear framework or measurable outcomes of mentoring relationships. The distinct expectations of a “Lead Mentor,” “Research Mentor,” and “Career Mentor,” which may be considered the norm40 Data from the Consortium for Advanced Research Training in Africa (CARTA) have also indicated that while researchers in the global south spend much time in research support, their research involvement is unrecognized and ill-compensated.36 These findings stress the need for fostering institutional environments to prioritize and support research within Africa. Participants in our study proposed non-monetary incentives for research productivity including credits in promotion evaluations, or opportunities to travel for the dissemination of research results. This theme highlights the inherent motivation to participate in research and provides concrete suggestions for modifications to the institutional environment and culture to support research works.Lack of research recognition, protected time, and low remuneration for research and activities were identified by both faculty and established cancer researchers as barriers for research. This is consistent with a report from 27 African countries, which showed that a shortage of staff in research and academic institutions makes it difficult for academics and clinicians to dedicate time for research.40The effective conduct of research requires an adequate research support environment including pre-post award support, financial management, and timely ethical review. Our assessment identified the existence of frameworks for research support in all institutions, each with different levels of capacity and performance. While pre and post-award services exist, they are understaffed and under-resourced to support all researchers across disciplines. Similar to what has been reported in other countries in Africa, the lack of human resources, guidelines, poor internet connectivity, software, and computers compromise the effective performance of these research support mechanisms.All participating IRBs faced various challenges, including a lack of effective operating guidelines, limited human resources, and continued reliance on hardcopy submissions. In Tanzania, the efficiency of the IRB is slowed by persistent dependence on hardcopies and the national charge to conduct both ethical and scientific review of submitted applications. Improving the quality of work submitted will also allow for IRBs in Tanzania to improve efficiency by reducing time required for rigorous scientific review.41 Similarly, a review of research capacity in Africa has also shown that most available research training opportunities, including those supported by international institutions or donors, emphasize knowledge rather than research skills and do not prioritize sustainability.42 As we look to establish cancer research training pathways in Tanzania, we emphasize the need for local investment to ensure sustainability. While supports from international collaborations may serve to boost research in Africa, these collaborations should be formed as transparent and equitable partnerships, where both parties develop rules of engagement with clearly defined bidirectional benefits. Research priorities should be guided by local needs, and collaborative research should begin from the conception of research design with all involved parties with mutual respect and sharing experiences and knowledge.43There is a need to mobilize local funds to support cancer research in Tanzania. Established cancer researchers emphasized that the focus of research funded by external sources is often determined by the funder or international collaborator, rather than by local priorities.Pathology and other diagnostic and treatment technologies as well as new technologies and approaches for conducting novel clinical research vary by institutions in the country. However, the proposed inter-institutional collaborations for example the national laboratory, MNH and MUHAS which have some state-of-the-art research, diagnostics and treatment technologies has potential to improve research and diagnosis and treatment for better clinical outcomes. In order to capitalize on these collaborations there is a need for research training as the one proposed in this work, to be able to build a cadre of researchers who will be able to design and evaluate new technologies which are context specific and could be scaled up at low cost. We therefore hope the established research training will be able to foster and train a pool of mentors and clinical researchers in cancer and hence improve not only on the quantity but also the quality of cancer research with ultimate goal of impacting the cancer care continuum in the region and beyond.44 This marks an important step towards the establishment of a national coordination unit for NCD and cancer research in Tanzania and reflects that cancer research is indeed a national priority in Tanzania. As we look towards the establishment of a D43-funded cancer research training program in Tanzania, investment in the development of rigorous research training, mentorship resources, and research infrastructure will be critical components towards empowering local health professionals to pursue high quality research activities. While the motivation to pursue careers in cancer research is high amongst both academic faculty and trainees in Tanzania, there is a critical need for national and institutional leaders to explore strategies and define metrics for invoking changes in clinical and academic culture to ensure and enforce protected time for research and to emphasize and reward research productivity.Cancer is contributing to an increased burden of disease throughout East Africa and has been named a priority area in Tanzania’s national research agenda. In November 2019, following completion of this analysis, the MoHCDGEC launched the NCD Program to oversee the planning, budgeting, implementation, and coordination of NCD.