Though the knowledge base on implementation strategies is growing, much remains unknown about how to most effectively operationalize these strategies in diverse contexts. For example, while evidence shows that champions can effectively support implementation efforts in some circumstances, little has been reported on how to operationalize this role optimally in different settings, or on the specific pathways through which champions enact change.This is a secondary analysis of data from a pragmatic trial comparing implementation strategies supporting the adoption of guideline-concordant cardioprotective prescribing in community health centers in the USA. Quantitative data came from the community health centers’ shared electronic health record; qualitative data sources included community health center staff interviews over 3 years. Using a convergent mixed-methods design, data were collected concurrently and merged for interpretation to identify factors associated with improved outcomes. Qualitative analysis was guided by the constant comparative method. As results from the quantitative and initial qualitative analyses indicated the essential role that champions played in promoting guideline-concordant prescribing, we conducted multiple immersion-crystallization cycles to better understand this finding.Five community health centers demonstrated statistically significant increases in guideline-concordant cardioprotective prescribing. A combination of factors appeared key to their successful practice change: (1) A clinician champion who demonstrated a sustained commitment to implementation activities and exhibited engagement, influence, credibility, and capacity; and (2) organizational support for the intervention. In contrast, the seven community health centers that did not show improved outcomes lacked a champion with the necessary characteristics, and/or organizational support. Case studies illustrate the diverse, context-specific pathways that enabled or prevented study implementers from advancing practice change.This analysis confirms the important role of champions in implementation efforts and offers insight into the context-specific mechanisms through which champions enact practice change. The results also highlight the potential impact of misaligned implementation support and key modifiable barriers and facilitators on implementation outcomes. Here, unexamined assumptions and a lack of evidence-based guidance on how best to identify and prepare effective champions led to implementation support that failed to address important barriers to intervention success.ClinicalTrials.gov, NCT02325531. Registered 15 December 2014. Contribution to the literatureThese findings show that detailed, contextualized reporting of how implementation strategies operate is critical to replicating successful implementation outcomes.The implementation science literature lacks guidance on how to operationalize the champion role; these findings advance our understanding of how to effectively do so.The findings suggest that reflexivity, or the active querying of assumptions affecting decisions during the design phase—particularly as these assumptions relate to likely barriers and facilitators (determinants) to intervention uptake—is essential to avoid a mismatch between how implementation strategies are operationalized and setting-specific determinants.Implementation strategies are techniques designed to promote the uptake of clinical interventions into practice  by targeThe Study of Practices Enabling Implementation and Adaptation in the Safety Net (SPREAD-NET) involved a set of implementation strategies, selected based on evidence demonstrating their ability to support practice change, their potential scalability, and pragmatic considerations . These strategies included (i) creating and distributing educational materials, (ii) conducting educational meetings and ongoing trainings, (iii) using train-the-trainer strategies, (iv) facilitation, and (v) identifying and preparing champions . The stuFor the manuscript presented here, we re-analyzed the study’s mixed-methods data to identify factors associated with differences in study outcomes in individual community health centers. As initial results indicated that the interplay between study implementers (clinic staff tasked with leading change processes) and organizational context was key to the success of a given community health center, we then explored the organization-specific pathways through which these implementers promoted practice change. We note that the use of champions –32 was oLittle has been reported on how to effectively prepare people for the champion role, or on the specific pathways through which champions enact change , 33, 34.The SPREAD-NET study compared the effectiveness of increasingly intensive implementation strategies at supporting community health centers’ adoption of a suite of clinical decision support tools called the cardiovascular disease (CVD) bundle. The CVD bundle included point-of-care alerts and panel management data tools promoting cardioprotective prescribing guidelines for patients with diabetes . It was Twenty-nine clinics managed by 12 community health centers in six states participated in the study . All study community health centers were members of OCHIN, Inc., a non-profit organization based in Portland, OR, that provides a shared Epic© EHR to > 600 US clinics. The community health centers were cluster-randomized into one of three arms; the arms received increasingly intensive implementation support designed to promote uptake of the CVD bundle , were indicated for a statin per care guidelines (denominator), and had a prescription for a statin (numerator), calculated monthly.The 3-year qualitative process evaluation was conducted from August 2015, through July 2018. Figure The evaluation design was informed by the practice change model , a conceThe secondary analysis of the SPREAD-NET data presented here occurred in two rounds. We first used the coding structure created as part of the parent study to conduct a preliminary exploration of the qualitative data by community health center  to identα of 0.05 and were conducted using SAS Enterprise Guide 7.15.As with the main study analysis , we usedCoding was conducted iteratively throughout the parent study. A preliminary code list was created by the lead qualitative researcher (AB) 1 year into data collection. This draft list was reviewed and refined by the three-person qualitative team into a codebook; each team member then coded an identical set of transcripts and compared the results to identify items needing revision. After agreed-upon codes were applied consistently across all coders, independent coding assignments were made. The team double-coded five percent of the qualitative data as a quality check, resolving inconsistencies through team discussion and consensus and updating the codebook as necessary. Coding was conducted using the QSR NVivo software, guided by the constant comparative method , 46. CodWhen the main study analyses found that increasingly intensive implementation support did not yield additive benefits , we desiBased on this analysis, we identified a set of interconnected factors related to characteristics of the study implementer and the organizational context that differentiated those community health centers that did and did not demonstrate a significant increase in guideline-concordant statin prescribing in the quantitative results. We then conducted multiple cycles of immersion crystallization  in whichThe standards for reporting qualitative research (SRQR) was used to guide the reporting of qualitative findings  . This stFive community health centers demonstrated statistically significant increases in guideline-concordant statin prescribing over the course of the study Table .Table 2Engagement: Interest in and willingness to promote the interventionInfluence: Sufficient social capital to foster trust and the authority to prioritize implementation and stimulate practice changeCredibility: Conferred through prescribing privilegesCapacity: Time—and understanding of diabetes and cardiovascular care—sufficient to effectively advocate for the interventionSeveral factors, in combination, were associated with significant pre-post increases in guideline-concordant statin prescribing among the community health centers noted in Table At each community health center that demonstrated a significant improvement in study outcomes, one or two implementers emerged as the de facto champions . As noted in the “We defined organizational support as the creation of an environment within which implementation activities could be expected to be taken seriously by clinic staff. In many cases, this support was indicated by the selection of a staff member with the potential to be an effective champion . Community health centers that demonstrated organizational support also promoted the use of the CVD Bundle and/or guideline-concordant statin prescribing. While organizational support, or the lack thereof, could take many forms—as illustrated below—implementation success depended on both the presence of champions with the aforementioned attributes and the implicit or explicit backing of clinic leadership, and the interaction of the two.Community health centers that did not show a significant improvement in prescribing rates lacked either an emergent champion, and/or organizational support. Implementers at these community health centers often were less engaged with the study/CVD bundle; did not have adequate influence at their clinic to be effective change agents for this intervention; and/or did not have a clinical background (and therefore the credibility to affect provider care decisions). Two community health centers due for diabetes care; the intensity of this effort varied by clinic and care team.Implementation efforts were enacted by an experienced, well-regarded physician [credibility] who had practiced at one of the clinics in this organization for many years and had previously served as medical director for the community health center [influence]. This provider was interested in research and volunteered to fill the study implementer role; her schedule already included 4 h a week of administrative time designated to community and research work [capacity]. This gave her the flexibility to innovate in her own practice and effect change through one-on-one engagement with other providers, including sharing alternative workflows for diabetes care, within an organizational culture that afforded substantial provider autonomy. In addition to experimenting with different workflows for diabetes care, this physician also met individually with all providers at both clinics, including during new provider onboarding, for a short, over-the-shoulder introduction of the study and the CVD bundle, and demonstration of the tools [engagement].Early implementation activities were limited to an introductory presentation to providers and occasional reminders during staff meetings. This community health center replaced their study implementer approximately 1 year into the study, shifting the role to a provider who was newer to the organization but brought a wealth of experience in diabetes care [credibility]; both providers were appointed to the implementer role by organizational leadership. The community health center’s increase in guideline-concordant statin prescribing appeared to be due to a combination of awareness-raising efforts by this clinician, coupled with an increasing organization-wide emphasis on standardization and quality improvement throughout the study period [influence]. During this time, the organization was working on becoming an accredited patient-centered medical home, with an attendant focus on practice change capacity. The new implementer brought renewed focus to the statin guidelines, often discussing prescribing recommendations at organization-wide provider meetings . She also requested and received the addition of a link to a CVD risk calculator on staff computers, and notified providers when it was added to the CVD bundle .Implementation efforts were led by two clinical pharmacists, one of whom was the clinic’s director of clinical pharmacy. The pharmacy director chose to lead implementation activities at the main clinic; the second clinical pharmacist was asked to take on the role at the second clinic. Success at this site was largely due to the strength and credibility of the clinical pharmacy program at the organization, the dedicated time the pharmacists were able to spend supporting guideline-concordant statin prescribing [capacity], and parallel awareness-raising by clinical leaders that resulted in an increased emphasis on relevant prescribing guidelines by multiple influential actors at the organization. The clinical pharmacy department was well-resourced, trusted [influence], and guideline-focused. The clinical pharmacists incorporated updated statin guidelines into pharmacy protocols, met individually with patients to review their medications, reviewed patient charts for guideline-concordant prescribing [credibility], and followed up with providers in conversations or messages, as well as discussing the CVD bundle and relevant guidelines at clinic meetings [engagement]. The clinical pharmacists worked outside the daily patient encounter workflow, which challenged their ability to stimulate change at the provider and team level. However, the organization’s medical director and the chief operating officer, who had formerly worked as an RN at the organization, ensured that the statin guidelines were discussed and debated at provider meetings.Implementation efforts were coordinated by an RN who served as the director of quality improvement for the community health center; she volunteered for the role and acted as the primary study implementer for the largest of the four clinics taking part in the study. The remaining three study implementers, all of whom held patient-facing administrative roles, were asked to manage implementation activities at the other three clinics. The four study implementers initially met multiple times to plan their implementation approach, introduced the CVD bundle to care teams at all four clinics, and identified and worked with a single provider to initiate a pilot project using the CVD bundle within his own patient panel. These efforts, however, never gained momentum. Organizational support was diffused by other activities occurring at the community health center during the study period, including simultaneous participation in other quality initiatives, the opening of a new clinic, and major construction projects and upgrades across the organization. Although the director of quality improvement did demonstrate engagement, influence, and capacity, she did not see patients herself and as an RN would have been unable to prescribe statins [lack of credibility]. The three other study implementers did not have clinical backgrounds and lacked the influence, credibility, and capacity (particularly a deep understanding of diabetes and cardiovascular care) to persuade providers to change their behavior; the additional clinician implementer, a practicing physician, did not play an active role in supporting implementation activities.Implementation efforts at this community health center, a single clinic, were enacted by an RN diabetes care manager who was assigned to the study implementer position. She conducted a few initial presentations about the CVD bundle at provider meetings and occasionally discussed the study’s EHR tools with individual providers. However, as this RN personally met with most of the clinic’s patients with diabetes in her role as a diabetes care manager, most of her implementation activities focused on her own activities. She integrated review of statin prescriptions into her pre-visit chart review process and follow-up visits with diabetic patients, and followed up with the primary care provider to discuss a plan of care based on clinical recommendations. The effectiveness of this approach was limited because (i) the EHR tools were designed to fire during open encounters, not during pre-visit chart reviews; and (ii) the approach relied almost entirely on a single person without prescribing privileges [lack of credibility], with little effort put into raising awareness or buy-in from providers. In addition, although this study implementer actively worked to identify patients with diabetes who could benefit from a statin [engagement] and had the influence and capacity to advocate for the intervention, she occasionally expressed distrust in the guidelines underlying the CVD bundle and/or the algorithm behind the EHR tools. Finally, although this clinic had a standardized process for piloting and approving new interventions and workflows, this process was not applied to the clinic’s participation in SPREAD-NET—which appeared to limit organizational awareness of and support for implementation activities.Our study adds to implementation science by providing insight into the pathways through which champions may impact implementation outcomes, and advances understanding of how to identify and prepare implementers to be effective champions within their own particular environments. Notably, unlike the community health centers that did not demonstrate a significant change in prescribing behavior, the five community health centers that did have improved outcomes all had engaged and respected champions (study implementers) who were able to directly influence provider behaviors or alter institutional prescribing norms. This suggests that not only the selection of champions as an implementation strategy but also the appropriate operationalization of support , are necessary for effective practice change; this is likely to also apply to other implementation strategies, and further research is needed to identify best practices for doing so. These findings also align with those of other studies that showed the potential for champions to successfully support introducing and maintaining practice change , 49–52.Implementation science emphasizes that identifying effective implementation strategies involves understanding the context-specific causal pathways through which these strategies can have impact , 34. TheThese findings also help explain the overall study results (higher-intensity implementation support was not associated with better outcomes). A recent review  found thRecognizing this led us to question the process through which we initially selected and operationalized the implementation strategies compared in the SPREAD-NET study. After a period of reflection  and exteSocial scientists have long argued that articulating tacit assumptions—beliefs that “you accept as true without question or proof” —is necesIn addition, despite strong evidence on the importance of champions in implementation activities, little direction exists on how best to support/develop/prepare champions. Often, the literature implies that effective champions have certain intrinsic qualities that cannot be taught , 31, 37.Our findings also indicate that implementation science theories and frameworks that involve the use of champions should be refined to include detailed specifications on both necessary intrinsic champion attributes and guidance on developing and supporting effective champions. The analysis presented here contributes to the growing knowledge base within the field regarding what makes an effective champion , 49, 57;All of the study community health centers volunteered to participate, and may have shared unique motivations that limit the generalizability of study findings. Qualitative data collection did not occur evenly across community health centers due to lack of engagement and staff turnover at some community health centers, as well as the mid-study closure of one organization. Findings are presented at the organizational rather than clinic level; it is possible that a single clinic within a community health center may have driven the change in cardioprotective prescribing, although we believe this is unlikely. A major finding of the original analysis was that aspects of the CVD bundle itself proved a barrier to implementation . It is pThis analysis adds to implementation science’s call for better approaches to selecting and operationalizing implementation strategies suitable to a given context , 15, 26.Additional file 1. SRQR guidelines for reporting qualitative research studies.


Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient’s chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care. Fortunately, from 2011 to 2015, there has been a small but prominent decrease in death rates for all races/ethnicities combined for 11 out of 18 most common cancers among men and 14 of the 20 most common cancers among women. The continued decreases in death rates for colorectal cancer, prostate cancer and female breast cancer are largely due to advances in early detection and more effective treatments2. In this review, we will focus on the computational challenges of identifying the best treatment that improves chances of successful recovery.Cancer is a leading cause of death worldwide and the most important impediment to increasing life expectancy in every country of the world in the 21st century5 that takes into account patients’ genomic makeup for treatment decisions7. Treatment approval based on tumor-site agnostic molecular aberration biomarkers has become reality. The year 2017 marked the first FDA approval of such a treatment8. Based on clinical trials in 15 types of cancer, pembrolizumab was approved for treatment of solid tumors with mismatch repair deficiency or high microsatellite instability9. Larotrectinib is another promising treatment, targeting the tropomyosin receptor kinase gene fusion in a variety of cancers10. Unfortunately, there are no established biomarkers for majority of the anticancer drug compounds. Identification of reliable biomarkers is a challenge not only for the most commonly used cytotoxic drugs, but also in the case of targeted therapies as the drug targets alone are generally poor therapeutic indicators12.Until recently, treatments were chosen based on the type of cancer in a one-size-fits-all manner. We are now witnessing the advent of precision oncology13 and preclinical14 settings. As predictive models increase in complexity, the number of observations required to train these models increases as well. While omic profiles and clinical outcomes of patients are the most relevant data sources for the development of clinically relevant predictors, these datasets are often limited in size due to many factors including high costs, limited accrual rates, and complex regulatory landscape. In addition, by the nature of the experiment, unbiased testing of multiple therapeutic strategies for the same patient in the patient itself is practically infeasible. Cancer models provide access to patient tumors in preclinical models, both in vivo and in vitro, allowing researchers to test multiple drugs and combinations in parallel14. Although these preclinical models recapitulate patient therapy response to varying degrees, they provide massive amounts of pharmacogenomic data for drug response prediction. Here we review the recent applications of machine learning to prediction of response to monotherapies and identification of combination therapies  derived by immortalization of human cancer cells became popular experimental bases for discovery of multi-omic underpinnings of drug sensitivity and resistance16. Since this seminal study, multiple large-scale databases have been publicly released to the cancer research community18. More recently, advances in growing tumors in animal models enabled the generation of large collection of patient-derived xenografts (PDX) to monitor tumor growth with and without drug treatment in mice19. Novartis published the largest PDX-based pharmacogenomic dataset to date, referred to as the PDX Encyclopedia20. The NCI recently announced the Patient-Derived Models Repository (PDMR) with comprehensive molecular profiling and commitment to release pharmacological profiles in the future. A series of databases and tools have been developed recently to harmonize and make easily available multiple pharmacogenomic studies investigating anticancer monotherapies  screened with tens of thousands of chemical compounds, including a large panel of FDA-approved drugs21 and MAMMAPRINT22 models for breast cancer are based on a linear regression model and a nearest centroid model, respectively). Thus, this review focuses on academic approaches to drug response prediction since they significantly outnumber commercial approaches, are more transparent, and address the more difficult task of predicting the efficacy of multiple drugs without knowing ahead of time the useful features for the task.The availability of commercial drug response prediction approaches is limited. In fact, publicly available methods mainly consist of biomarker assays which measure quantities such as gene expression and determine whether or not a specific therapy linked to the biomarker assay would be effective for a given patient. Most of these assays and predictive models are univariate, with only a few multivariate assays that are based on simple statistical and machine learning approaches  in addition to architectures that mimic the human brain and limit overfitting such as convolutional neural networks. Developing neural network architectures with an effective inductive bias for genomics will allow the complex underlying cancer biology to be better modeled compared to linear models which reduce risk of overfitting at the cost of introducing significant modeling bias. Another approach to deal with the limited number of samples typically available in drug response prediction experiments is feature selection. Feature selection removes features such as the gene expression of genes which are determined to be uninformative for the phenotype being predicted. This improves the ratio of features to samples, and a common to feature selection is univariate feature selection where only features highly corrected with the phenotype are kept. Multivariate approaches to feature also exist and consider sets of features at a time since any single feature individually might not be predictive of the outcome, but that does not imply that a collection of features is uninformative as well. Papillon-Cavanagh et al.31 identified univariate feature selection as a robust selection approach, later improved by minimum Redundancy, Maximum Relevance (mRMR) Ensemble feature selection32. Costello et al. and Jang et al. performed extensive comparative analyses of machine learning methods for drug response prediction in cancer cell lines, recommending using elastic net or ridge regression with input features from all genomic profiling platforms29. Costello et al. summarized a crowdsourced DREAM drug prediction challenge29, revealing two leading trends among the most successful methods. First, the importance of the ability to model nonlinear relationships between data and outcomes, and second, the incorporation of prior knowledge, e.g. biological pathways. The challenge winning model, Bayesian multitask multiple kernel learning method33, incorporated both of these approaches together with multi-drug learning34. Such multitask framing of the prediction problem is highly effective as it enables a more efficient use of available data when tuning parameters. Specifically, instead of building separate prediction models for each drug thereby using just a subset of the data, a single model trained with all the data that has some parameters shared amongst all the drugs, and some drug-specific parameters is the better choice.The most typical computational approaches to drug response prediction, specifically in preclinical models, consist of (1) quantification of drug response; (2) molecular feature selection or dimensionality reduction of the cellular measurements; (3) machine learning model fitting to predict drug response; and (4) model evaluation35. Furthermore, models encoding prior biological knowledge have improved and more stable feature selection since noisy gene-level measurements can be abstracted into gene sets that have been experimentally validated to be involved in cancer-related processes. Lee et al.36 developed a method that integrates disease relevant multi-omic prior information to prioritize gene-drug associations. Most recently, Zhang et al.37 and Wang et al.38 introduced methods based on similarity network fusion and similarity-regularized matrix factorization, respectively, that take into account similarity among cell lines, drugs and targets. Drug chemical features and similarities were shown to be a promising additional information that can improve drug response prediction performance. There is no canonical way of incorporating drug features into most predictive models since it is difficult to encode how the drug features and omics features interact. Future models that address this shortcoming are likely to outperform competitors that do not, due to the highly informative content of molecular fingerprints. Specifically, a predictive model in a multitask setting can take compounds with known molecular targets, use the similarity computed between the molecular fingerprints, and more effectively tune parameters using similarity between compounds for parameter regularization.Nonlinear relationships are of utmost importance since many cellular processes follow nonlinear dose-response relationships such as the activation of MAPK via Progesterone in oocytes39. Neural networks, however, did not become a method of choice for monotherapy prediction yet. In fact, despite the recent prevalence of deep neural network (DNN) methods across many areas and industries, including related fields, such as computational chemistry45, DNNs have only fairly recently found their way into the drug response prediction. The reason for this is the typically low ratio of the number of samples to the number of measurements per sample that does not favor traditional feedforward neural architectures. Overparameterization in these models easily leads to overfitting and poor generalization to new datasets. However, in recent years, more public data has become available and newly developed deep neural network models are showing promise. For example, Chang et al.46 developed the CDRscan model, featuring a convolutional neural network architecture trained on a dataset of ~1000 drug response experiments per compound. Their model achieved significantly improved performance compared to other classical machine learning approaches such as Random Forests and SVM. Part of why CDRscan performed better than these baseline models resides in its ability to integrate genomic data and molecular fingerprints. In addition, its convolutional architecture has shown to be effective in many machine learning domains. Taking inspiration from already well-established neural architectures, and modifying their structure to properly handle genomic data is certainly a promising future direction.The use of neural networks for drug response prediction dates back to the 90s. El-Deredy et al. showed that a neural network trained on tumor nuclear magnetic resonance (NMR) spectra data has potential as a drug response predictor in gliomas, and may be used to provide information about the metabolic pathways involved in drug response47 where a 5000 dimensional gene expression profile was compressed into just 100 dimensions, some of which represented phenotypically relevant features such as patient sex or melanoma status. Rampášek et al.48 evaluated semi-supervised variational autoencoders on monotherapy response prediction and developed an extension—a joint drug response prediction model, Dr.VAE, that leveraged pre- and post-treatment gene expression in cell lines, showing improved performance in drug response prediction on a variety of FDA-approved drugs compared comprehensively to many classical machine learning approaches. This improvement could potentially have been even greater if the model was setup in a multitask fashion in combination with molecular fingerprints. Dincer et al.49 developed DeepProfile, a method that combined variational autoencoders to learn 8-dimensional representation of gene expression in AML patients and then used this representation to fit a Lasso linear model for drug response prediction with improved performance compared to no feature extraction. Similarly, Chiu et al.50 pretrained autoencoders on mutation data and expression features on TCGA dataset and subsequently trained a deep drug response predictor. What differentiates their method from others is the use of pretraining. Pretraining allows for using unlabeled data from other sources such as TCGA, instead of just the gene expression profiles available from the drug response experiments, thereby significantly increasing the number of samples available and improving performance compared to using just the labeled data. The brief summary of methods is available in Table Another promising direction is autoencoders that are able to learn from smaller datasets. An autoencoder is a neural network that compresses its input and tries to reconstruct the original data from the compressed representation. This is quite useful for feature extraction as shown by Way and Greene51. Understanding the common mechanisms cancers use to develop resistance can help inform treatment approaches to counteract this phenomena.While drug response prediction can help pick an optimal therapy given the current molecular characteristics of the cancer cells, tumors often exhibit drug resistance over the course of the treatment. Consequently, patients that respond initially to therapy regress as their cancer either adapts to overcome the chosen treatment, or an existing resistant subclone repopulates the tumor52, with corresponding overproduction of TS enzyme and mRNA transcripts53. Furthermore, especially for tyrosine kinase inhibitors, tumors will evolve to re-activate pathways downstream of the targeted protein. A classical example is the resistance to the EGFR inhibitor Gefitinib which can often be explained by an acquired T790M mutation reducing drug binding affinity54.For therapies inhibiting the activity or signaling of their target, a common mechanism towards resistance is feedback selecting for upregulated expression of the target protein. For example, resistance to 5-FU has been demonstrated to arise from the amplification of its target thymidylate synthase (TS)56. Furthermore, studies suggest that secondary alterations to DNA damage response proteins can shift the response from the error-prone Non-Homologous End Joining pathway to HR, reducing sensitivity to DNA damaging agents57. Other mechanisms of resistance include modifications to enzymes involved in drug metabolism to either reduce conversion of drugs to active forms or deactivate the compound59, and more recently, intra-tumor heterogeneity (ITH)60. As this review is focuses on drug response prediction, not enough depth is provided to discussing how tumors acquire resistance to therapies, or how therapies work. Readers are referred to work by Holohan et al.51, Housman et al.59, and Malhotra and Perry61 for a comprehensive discussion on this topic. For more details on the biological complexity of cancer in general, readers are referred to the review articles by Blackadar62, and Bertram63.For DNA damaging compounds or compounds inhibiting DNA repair, altered DNA damage response can lead to resistance. Studies have shown that treatment with cisplatin, a DNA damaging agent usually effective against BRCA deficient cancers, can lead to mutations restoring BRCA function and subsequently the activity of the Homologous Repair (HR) pathway64.Drug combinations are crucial for addressing the issue of drug resistance and preventing recurrence caused by a negligible amount of remaining cancer cells. Synergistic combinations can also reduce toxicity by allowing for lower doses of either drug to be used. By enabling reduced doses, drug combinations can further increase the feasibility of drug repurposing by increasing the potency of compounds that are only effective at clinically dangerous doses65 performed a clinical trial for metastatic colorectal cancer (mCRC) patients involving the targeted compound bevacizumab, either oxaliplatin or irinotecan as a chemotherapeutic agent, and an optional addition of a human antibody panitumumab. The purpose of the trial was to evaluate benefit conferred by panitumumab. It was revealed that for the cohort that used oxaliplatin as a chemotherapeutic agent, survival was 5 months lower for patients that also received panitumumab, and there was a significant increase in adverse effects such as infections and pulmonary embolism compared to patients that did not receive panitumumab. Tol et al.66 also performed a clinical trial for mCRC, using combination of capecitabine, oxaliplatin, and bevacizumab, as the baseline treatment to investigate cetuximab. Patients that received cetuximab had a shorter progression-free survival and reported significantly more adverse effects compared to patients that did not receive cetuximab.Trial and error combination design has limited applicability in the clinic due to time constraints and potential hazardous exposure to toxic combinations without improving efficacy. For example, Hecht et al.67. The probabilistic ranking given by their method potentially allows for identifying when tumors develop drug resistance by analyzing when drug combinations are given priority over individual treatments. While this work, performed on PDX, learns more complex yet more effective policies in terms of survival than currently offered in the clinic, it is not clear how to mitigate the potential risks of exploration needed for reinforcement learning. We do hope that this direction is given its due consideration in the clinic since these early results appear to be very promising.One promising direction for a setting where the goal is to study a constrained set of options to design an optimal treatment plan for a patient is adaptive trials via reinforcement learning68, so there is a need for methods that can predict combination therapy response prior to experimentally validating it.The limits of trial and error in the clinic can also be overcome in vitro with the use of preclinical models in the form of immortalized cancer cell lines or cell lines derived from patient biopsies. Patient-derived cancer models allow screening drug combinations in parallel without subjecting patients to serious toxicity risk Table . Unfortu69. This gives rise to an 8×8 dose-response matrix. Using a 384-well assay plate, six pairs of drugs can be screened at once in this arrangement. Once cells are incubated in the wells for a sufficient amount of time, usually 72 h, a cell viability readout is conducted to determine the number of viable cells in each well. The collected data is then processed using a tool such as SynergyFinder70 to quantify the drug combination response compared to individual compound response based on a variety of models. As an example, the Bliss independence model71 provides a score under the assumption that the two drugs act independently, so measurement above this score indicates synergy. For more details on different synergy scores as well as experimental design of drug combination studies, the reader is referred to the experimental design guide by He et al.69. The number of experiments increases exponentially with the number of drugs tested in combination, making these combination screens both logistically complex and expensive. It is therefore favorable to have a method which does not require significant amounts of combination screens. Several approaches for drug synergy prediction described in the literature instead use a combination of either perturbation experiments or sensitivity experiments coupled with drug target and drug structure data. For example, the work done by Li et al.72 leverages gene expression perturbation data, measured as the difference in gene expression before and after treatment, to compute various statistics about differentially expressed genes as the main pharmacogenomic features. Additionally, the authors extracted drug physicochemical properties, distance between drug targets in PPI networks, and Jaccard similarity between targeted pathways to represent biological and chemical prior knowledge. These features were then used to train a random forest model to perform the binary prediction task of whether a drug combination is synergistic or not. Gayvert et al.73 also made predictions with random forests by using both single-drug response values and combination therapy response values when available. Interestingly, they did not leverage drug structure information nor gene expression profiles when making predictions. This is a drawback since drug structure information is easily available, and including it may improve performance, but it provides flexibility in not having to measure gene expression. However, their framework is broadly applicable, and their results indicate that even a small number of drug combination experiments can have a great performance benefit when used to train a model that makes predictions using primarily single-drug response data.Many computational methods have been developed to predict anticancer drug combination synergy based on a variety of genomic, drug structure, and biological network data. These methods vary in how much drug combination screening data is required, if used at all. Drug combination screening data refers to testing cancer models with combinations of two or more drugs rather than a single drug. A typical combination experiment setup involves testing two drugs at 8 different half-log dilution concentrations each including the null concentration as a control74 and Nowak-Sliwinska et al.75 use Feedback System Control (FSC) to iteratively refine drug combinations and suggest new ones to test in vitro. The process works by first starting with some randomly selected drug combinations for some range of doses. This group of combinations is then mutated using Differential Evolution (DE) to propose new drug combinations that are to be tested in vitro, and whose efficacy will be compared against the original randomly selected combinations. For each mutated combination, if that combination had higher efficacy than the original random combination that it was created from, then the new combination is kept, otherwise the original combination is kept. This procedure is repeated until some convergence criterion is met. This approach seems to be very effective in practice because the efficacy versus drug combination surface is smooth thereby allowing FSC to converge in 10–15 iterations. Lastly, the optimal drug combination identified by DE and evaluated in vitro is further optimized to eliminate redundant compounds or compounds having an antagonistic effects. Importantly, FSC based approaches are not limited in the number of drugs used in a given combination, unlike many methods that are created specifically for pairs of drugs. It might be possible to accelerate the convergence of FSC methods by including genomic or chemical data since both methods described above perform the optimization without considering drug targets or drug similarities.There is a class of drug combination optimization approaches that interacts with the user by suggesting promising combinations to test. Both Weiss et al.76 where the authors only leverage transcriptomic data and drug structure data to predict Loewe score which quantifies the excess over the expected response if the two drugs used in a combination were the same compound. What further differentiates this work from previous works is that the authors use deep learning to achieve state-of-the-art performance compared to baseline models such as gradient boosting machines, random forests, and support vector machines. Xia et al.77 used deep learning as a means of simultaneously extracting and integrating features from multiple data types to predict the efficacy of drug pairs. Combination response data as well as gene expression, microRNA, and protein abundance from the NCI-ALMANAC dataset was used78. Additionally, drug features were obtained using Dragon software79 which provides chemical fingerprints and other properties. Each data type was passed through its own submodel where a submodel is just a deep fully connected neural network in order to obtain useful features and perform dimensionality reduction. Then, these features for the different data types were concatenated and passed through a final submodel that uses residual connections in order to predict the drug combination score. Ultimately, the authors were able to obtain impressive results with R^2 of 0.92, and much of that explained variance was due to the drug descriptors. These approaches reinforce the importance of newer deep learning methods such as molecular graph convolution to extract task-specific molecular fingerprints. A summary of tools related to drug combinations is provided in Table The most extreme prediction scenario is to not use drug response data at all when building a model. This is done by Preuer et al.80. These data can be used to directly reconstruct the unique genotypes as well as to estimate the clonal fraction within the sample. Bulk DNA sequencing does not have these abilities, so simply identifying populations of cells with different mutations can already significantly improve treatment plans  is starting to be used to design novel drug combinations through identifying druggable subclones87. Unlike DNA, where each cell contains only one copy of each allele , there is approximately 30 pg of RNA in a single cell. With the advent of the Chromium platform it is also now possible to sequence the RNA across 100,000s of cells in a single experimental run88. Predictive models of drug response could be developed and trained using high-throughput preclinical pharmacogenomic data, and an optimization framework to predict the most efficient and the least toxic combination treatment could be established.The development of single-cell sequencing technologies has given researchers a new set of tools to interrogate tumor heterogeneity. Single-cell DNA sequencing (scDNAseq), can be used to more directly investigate the clonal structure of a tumor. It works by isolating individual cells and performing whole genome amplification to increase the amount of DNA present in order to be detectable by a DNA sequencerns Table . However89. They first performed single-cell sequencing on four different cell lines derived from lung adenocarcinoma to compare the relative divergence in their gene expression profiles. Even though the average gene expression levels were generally similar, the relative divergences between cell types were pronounced. To investigate how targeted therapy affects individual cells, they treated LC2/ad cell line and the derived resistant version of it with vandetanib, a multi-tyrosine kinase inhibitor. The comparison of single-cell profiles of treated cells versus parental cells identified a wide variety of genes overexpressed by drug stimulation. Particularly in case of LC2/ad, the diversity level of gene abundances between cells was significantly reduced after treatment so authors hypothesized that cells lose diversity in response to treatment. Interestingly, target genes of vandetanib, EGFR and RET, were not as affected by the treatment as some of the other off-target genes possibly due to the rigid transcriptional controls over these targets.One of the first analyses to examine the influence of treatment on the transcriptome of cancer cells at single-cell resolution was conducted by Suzuki et al.90 sequenced the transcriptome at single-cell resolution of a primary renal cell carcinoma (pRCC) and its lung metastasis (mRCC) from a patient and paired PDX models to design a combination therapy that would address the heterogeneous nature of the tumor. Whole exome sequencing of the metastatic sample and its PDX model indicated the preservation of major tumor features in the PDX model. In order to predict single-cell response of the RCC to the clinically approved drugs, activity of drug target pathways was estimated by conducting gene set enrichment analysis. Subsequently, cell lines derived from the PDX models were screened with the drugs. Predictive drug response models, based on ridge regression, were built using expression profiles of cancer cell lines from a publicly available drug screening dataset92 to predict response to the drugs. Authors used ComBat to remove the technical variation between the cell line dataset used for training, the drug response predictors, and single-cell RNA-seq data. Predicted drug response values were substantially correlated with measured sensitivity values (0.65). Accordingly, by considering high sensitivity prediction of cells to Afatinib and Dasatinib and mutually exclusive patterns in the activation status of their signaling pathways in cells, the authors suggested a combination of these two compounds as an efficient therapeutic strategy. In vitro validation in 2D and 3D cultured mRCC cells and in vivo validation in subcutaneous xenografts validated the expected additive effect of the drug combination over monotherapy responses. The administration of this combinatorial therapy is inducing superior growth inhibition by co-targeting mutually exclusive EGFR and Src signaling pathways.Kim et al.90 work is the low number of single cells sequenced. The captured cells may not reflect the true clonality of the patient tumor and might even lead to false discoveries. Recent technological advances in single-cell sequencing made it feasible to capture large numbers of single cells in one experiment. New computational pipelines and approaches have been developed to improve all the steps in processing of the single-cell sequencing data94, including tackling noise and dropout in these experiments, normalization techniques, dimensionality reduction97 and clustering approaches99. These rapidly evolving methodologies provide remarkable opportunities for the discovery of biomarkers, prediction of efficient therapies, and the study of mechanisms of acquiring resistance to treatments.One of the major weaknesses of Kim et al.’s100 were the first to use single-cell perturbation experiments to optimize drug combinations. Their model DRUG-NEM required the specification of lineage, intracellular communication, and apoptosis markers that were measured in drug perturbation experiments using Mass Cytometry Time-of-Flight (CyTOF). The objective of the model is to select the minimum number of drugs that creates the maximum perturbation effect on the markers of interest using perturbation data from single-drug experiments. Drug effects were measured using a Bayesian linear model to compute the probability that an intracellular communication marker is differentially expressed between treatment and control. A graphical model is then created from these probabilities using a nested effects model, and all the possible drug combinations are ranked. This approach is limited by having to know ahead of time which markers to use, and this in turn requires knowing the mechanisms of action for the drugs, which in many cases is not available. Nevertheless, this direction for drug response prediction is very promising and will be greatly aided by the burgeoning single cell and drug clonality research.Anchang et al.101. There is also a need for better data sharing as technical replicates are necessary for estimating within-study variability, yet are sometimes not publicly released102. Additionally, the studies use a variety of datasets, thereby making quantitative comparisons even less feasible. Instead, qualitative comparisons are made between the methods that consider data requirements, generalization ability, and capacity to model complex biological interactions and chemical interactions. These comparisons are of great practical use as they provide context and scenarios in which one method is likely better than another.The only standardized metric to date for cancer response is RECIST, and it relies on imaging data, mainly CT and MRI, to determine how tumors grow or shrink in patients. RECIST can handle up to 10 lesions in the patient, prioritized based on the largest lesions, and uses the sum of the lesion diameters (LD) when first measured as the baseline value. In subsequent scans, response is categorized into 4 different categories based on how much the sum of LDs has changed: complete response, partial response, stable disease, and progressive disease. There is no such international standard used to measure response for in vitro preclinical models and RECIST is usually not used in in vivo preclinical models due to costs, thus prohibiting fair comparisons between response prediction methods. Furthermore, some drug response prediction studies frame the task as regression where continuous values such as IC50 are predicted, and others frame the task as classification where a binary value which indicates inhibition or growth is predicted. Reproducibility between cell line based drug response studies remains a challenge due to differences in viability assays, drug concentrations, and cell seeding density103, or conditional scaling which allows for multiplicative relations between features such as a mutation being required for gene expression levels to be relevant. The nonlinear nature of deep neural networks, combined with their inductive bias that allows them to generalize even though they have many more parameters than samples, suggests that promising applications are possible in pharmacogenomics where complex correlation structures exist among features and between features and labels. For example, graph convolutional networks are a promising new way of encoding structural information from molecular graphs104 and can give application-specific chemical fingerprints that are more specialized for drug response or combination therapy discovery. Another fruitful direction is the use of transfer learning to leverage an abundance of omics data already available. The main obstacle for transfer learning is the large discrepancies between the techniques and experimental protocols used for different studies which lead to batch effects that violate the assumptions on which deep learning relies to generalize to new datasets. The creation of domain adaptation techniques, similar to computer vision105, specific for omics data will be of immense help in enabling transfer learning. Still, creating architectures with an effective inductive bias for processing omics data is difficult since it is not possible to just rely on the human brain for inspiration like in image analysis. Thus, neural architecture search techniques which remove humans from the design loop by automating the creation and testing of architectures are of key importance in making deep learning more successful in drug response prediction106. It has recently been shown that the success of architecture search techniques depends significantly on careful design of the search space107. This requires encoding prior knowledge about potentially effective architecture choices which is certainly less difficult than specifying an entire architecture, but still remains challenging. Deep learning can certainly help in better understanding cancer biology by predicting binding sites or discovering new biomarkers by analyzing RNA transcripts109. In fact, deep learning has also been used to predict protein-protein interactions109 which are of increasing interest as potential targets for cancer therapies110, so deep learning will have an impact on both drug discovery and drug response prediction.The success of deep learning across scientific fields followed the collection of large standardized datasets. An additional factor important to broad utilization of deep learning was the growth in available computational power for training these models. Similarly, successful applications of deep learning in predictive oncology followed the growth of high-throughput preclinical datasets. This suggests that with additional data from studies that are more reproducible, deep learning could provide significant improvements over traditional machine learning methods in drug response prediction and drug combination prioritization. Specifically, the end-to-end nature of deep learning allows for extremely effective feature extraction and also enables the integration of multiple distinct data modalities. Additionally, encoding prior biological knowledge in neural networks can be done via several mechanisms such as graph-convolution networksThe problem of predicting the optimal treatment or combination of treatments for a cancer patient remains unsolved. The approaches reviewed above seek to bring recent advances in machine learning to bear on this challenge, leveraging the growing high-throughput preclinical screening data and new technologies allowing the profiling of tumors on a single-cell level. Promising results in this area should encourage both the investigators working on developing cheaper and more precise high-throughput screens to enable further data collection as well as ML method developers to develop novel tools incorporating peculiarities of cancer biology. While there remains much work to be done, the field is nascent and offers a path to a truly personalized approach to oncology.Supplementary Material


Irritable bowel syndrome (IBS), with its key features of abdominal pain and disturbed bowel habit, is thought by both patients and clinicians to be strongly influenced by diet. However, the complexities of diet have made identifying specific food intolerances difficult. Eating disorders can masquerade as IBS and may need specialist treatment. While typical food allergy is readily distinguished from IBS, the mechanisms of gut-specific adverse reactions to food are only just being defined. These may include gut-specific mast cell activation as well as non-specific activation by stressors and certain foods. Visceral hypersensitivity, in some cases mediated by mast cell activation, plays a key part in making otherwise innocuous gut stimuli painful. Rapidly fermented poorly absorbed carbohydrates produce gaseous distension as well as short-chain fatty acids and lowering of colonic pH which may cause symptoms in IBS patients. Limiting intake of these in low FODMAP and related diets has proven popular and apparently successful in many patients. Existing diet, colonic microbiota and their metabolic products may be helpful in predicting who will respond. Wheat intolerance may reflect the fact that wheat is often a major source of dietary FODMAPs. It may also be either a forme fruste of coeliac disease or non-specific immune activation. Wheat exclusion can be successful in some of these patients. More research is needed to fully understand the mechanisms of food intolerances and how to best ameliorate them in a personalised medicine approach to diet in IBS. Symptoms of the irritable bowel syndrome, including episodic abdominal pain and erratic bowel habit are ubiquitous and common in the general population . The comSince pain is required to meet Rome IV criteria , this teA characteristic feature of IBS is the variability in bowel pattern and pain. Patients report more variation in stool consistency and less predictability of their stool form from day to day . Daily dAmbulatory 24 h recordings make it clear that eating is a major stimulus to colonic contraction , which bThe reasons for pain in constipation is less clear though recent surveys have suggested that pain is more of a problem in IBS-C than other subtypes . The patThere are multiple possible mechanisms whereby eating could stimulate pain . These dHowever, mast cell activation can be limited to the gut, where their activation is more difficult to document and traditional allergy tests such as specific serum IgE or skin prick tests are negative. Recent studies show that after infectious gastroenteritis, mast cells in the murine gut can react to food given at the time of infection . ImportaThe time course of symptoms will vary depending on the mechanisms. At least some food-associated pain could be due to indirect stimulation of colonic motility by the act of eating. This would occur within 5–30 min, while effects due to colonic distension would be predicted to develop after 4–6 h, as food residue enters the colon. Onset between 30 min and 4 h might suggest a direct effect of food on the small bowel as demonstrated by recent studies using confocal laser endomicroscopy . Direct Impaired barrier function has been associated with both visceral hypersensitivity and pain severity in IBS patient with diarrhoea (IBS-D) though the precise mechanism is unclear . The samImpaired barrier function has also been noted as a stress response, which has been shown in humans to be mediated by activation of mast cells . These pWhether mast cell changes in the small bowel are also reflected in mast cells in the colon is unclear but an increase in colonic mast cells is one of the most consistent changes found in IBS  which hain vitro models using human faecal inoculates shows that speed of fermentation, total gas production and SCFA production slows as the DP increases ) . TypicalWhile attempts to show evidence of specific systemic food allergy using serum antigen-specific IgE antibodies and skin prick tests have largely failed to show differences between IBS and controls, it remains possible that gut-specific allergy is responsible for symptoms which follow soon after food ingestion. Recent animal studies suggest that exposure to ovalbumin during a bout of gastroenteritis can lead to a specific ovalbumin allergy limited to the gut with no IgE antibodies detectable in the serum . This maA search for simple blood indicators of food intolerance lead to a number of studies using food-specific IgG levels. Despite initial enthusiasm based on small clinic populations suggesting abnormal levels of IgG4 antibodies to food  subsequeThe role of lactose in causing symptoms in lactase-deficient individuals was clearly established in the 1970s with the demonstration that lactose caused an osmotically driven influx of fluid into the small bowel  and subsThis is based on a diet recommended by the National Institute for Health and Care Excellence (NICE) and the British Dietetic Association . While tFODMAP is an acronym for Fermentable, Oligo-Di- and Mono-saccharides and Polyhydric Alcohols, a term which neatly describes foods which often lead to symptoms of discomfort, flatulence and erratic bowel habits. This unifying concept covers both lactose and fructose as well as fructans and other poorly absorbed carbohydrates whose exclusion in the low FODMAP diet has gained tremendous popularity with both dieticians and patients world-wide . Dietary interventions are difficult to double blind and prone to powerful placebo/nocebo effects so it was not until the Melbourne group performed a double-blinded randomised placebo-controlled trial using clear solutions of test substances that it was conclusively proven that both fructose and fructans induce more symptoms including abdominal pain, bloating and flatulence in IBS patients than glucose alone . HoweverCross-over studies tend to show bigger differences than parallel-group designs because when subjects swap diets, changes are easier to detect and blinding may be broken. However, meta-analyses of a subsequent 7 RCTs including 397 IBS patients suggest a significant improvement with a low FODMAP diet when compared to both usual diet and a high FODMAP diet, the relative risk 95% CI) of not improving being 0.69 (0.54 to 0.88) . However% CI of nBacteroides genus, Ruminococcaceae family, and Faecalibacterium prausnitzii species, all known for saccharolytic metabolic capacity [Habitual intake is important since those who take very few FODMAPs, for example those already on a gluten-free, low-lactose diet may experience little benefit as they are already excluding most FODMAPs. Given that a low FODMAP diet is thought to reduce flatulence and bloating by inhibiting fermentation of FODMAPs, it is perhaps expected that the initial microbiome could influence response. One short-term feeding trial in children assessed the symptom response to a 2 day intervention of either a low FODMAP (maximum 9 g of FODMAPs/day) or TACD  (maximum 50 g FODMAPs/day) with a 5 day washout period between diets. The diet significantly reduced the number of pain episodes per day. Responders were defined as those achieving a 50% reduction in daily pain episodes on the low FODMAP diet but not the TACD. At baseline, responders compared to non-responders were enriched in the capacity . Functiocapacity . An alternative way of assessing the metabolic capability of faecal microbiota is by analysing the volatile organic compounds (VOC) in the head space of a bottle of stool heated to 50 °C for 10 min. This was used in a trial of a low FODMAP diet versus a sham diet in which response was defined as a fall of >50 on the BS Symptom Scoring System (IBS-SSS). The VOC profile at baseline was shown to be able to predict response rate using levels of 15 compounds that in principle components analysis could account for 25% in the variation in response to a low FODMAP diet . HoweverJust recently the exciting potential of genetic testing has been reported, with a reduced efficacy of low FODMAPs diet in patients with IBS-D carrying sucrase-isomaltase (SI) hypomorphic variants . These vAkkermansia mucinophilia. There are also reductions in absolute numbers of Bifidobacteria with increases in Ruminoccocus torques. Clostridia cluster IVa are important producers of butyrate [FODMAPs play an important role in providing nutrients for colonic microbiota which degrade them to short-chain fatty acids. Restricting intake as part of a low FODMAP diet reduces total bacterial numbers, particularly the relative abundance of the Clostridia cluster IVa within the family Lachnospiraceae as well as the species butyrate  which isbutyrate ,103. Bifbutyrate  includinbutyrate . They habutyrate , so reduOne concern about restriction of FODMAPs is that by depriving colonic bacteria of carbohydrate substrate, this will lead to a switch from saccharolytic to proteolytic metabolism, increasing the degradation of colonic mucin, particularly in the distal colon, with possible carcinogenic implications . These cWheat has consistently been identified as aggravating IBS symptoms, both as reported in patient surveys  and triaAfter excluding coeliac disease by serology, HLA typing and if necessary duodenal biopsy in patients reporting wheat intolerance, then one is left with non-coeliac wheat sensitivity . SuspiciSince the nocebo effect of taking food one believes one is allergic to is substantial , such paThe practical implications from these studies are that wheat exclusion may well be beneficial in up to 1/3 of IBS patients but that patients should be initially encouraged to simply minimise their intake of wheat rather than adopt a gluten-free diet, which is expensive and very restricting on social activities. Only if this fails are more elaborate immunological testing required, particularly if there is a family or personal history of atopy. While histamine elicits a number of serious adverse effects including most prominently headache, rash, urticarial, diarrhoea and hypotension , it seemRecent imaging studies have shown that lettuce and rhubarb both markedly increase water content of the small bowel and the ascending colon . This seIt is clear that taking a careful dietary history including not just what is eaten or avoided but also the meal pattern is important in managing IBS. Patient’s beliefs about what causes their symptoms must be explored since they may be correct, but equally they may need challenging if an eating disorder has developed. Once coeliac and other IBS-mimicking disorders such as inflammatory bowel disease and bile salt malabsorption have been excluded, dietary management should most economically start with the NICE guidelines. This will result in improvement in approximately 40%. If this fails, then there are many possible options. My personal approach is to start with a trial of a simple exclusion diet excluding just a few common items such as wheat, milk, excessive caffeine and/or specific foods that the patient has identified, which is a relatively easy first step. If this fails, then referral to a dietician for a trial of a low FODMAP diet is recommended, with particular emphasis on the need to complete the reintroduction phase of the diet so that the long-term diet is adequate nutritionally. This stepwise approach is both economical and will result in improvement in symptoms in a substantial proportion of IBS patients. Future research should focus on developing patient-acceptable, non-invasive ways of reliably identifying specific food intolerances, particularly wheat and milk protein intolerance, together with ways of reducing both immune- and non-immune-based adverse responses to food in IBS.


Ocular injuries and disruption to ophthalmic health-care services are common features of sudden-onset disasters, and often result in life-changing consequences for those affected. Each year, hundreds of millions of people are affected by large-scale disruptions, such as earthquakes or conflicts,,The World Health Organization (WHO) classifies emergency medical teams into three types. Type 1 provides outpatient services, and types 2 and 3, respectively, provide acute and complex inpatient care and have the capacity to accommodate additional specialized teams as required by the host country and the scope of the medical teams’ mission .2 As a rDuring such events, qualified health-care workers trained in the management of emergency eye conditions, including ocular trauma, normally provide ophthalmic care within pre-existing structures, with minimal added capacity. However, ocular conditions that non-specialists cannot deal with inevitably arise. In view of the potential for increased number of eye-care cases, severity of ocular injuries, and the breakdown of normal health-care provision, we advocate increasing specialist ophthalmic services within emergency medical teams.The role of emergency medical teams is to provide and maintain essential health care, including managing the direct consequences of the crisis, such as mass injuries and subsequent disease outbreaks, coincidental medical emergencies and pre-existing chronic health conditions. Conceivably, ophthalmic surgeons and allied health-care workers could contribute to the emergency response across all of these domains.The demand for ophthalmic care varies with the nature of the challenge. In armed conflict, injuries to the eye occur disproportionately to its surface area, and in recent conflicts the incidence of combat-related ocular injuries has continued to increase.Damage to health infrastructure severely affects health service delivery and can set back the improvement of a health system by years. For example, the Great East Japan Earthquake of 2011 triggered powerful tsunami waves and caused almost 20 000 casualties, widespread evacuation and complete or partial destruction of nearly 400 000 buildings along the coastline, including many hospitals.Ocular complications may precipitate an urgent need for ophthalmic care during outbreaks of contagious disease, which can vastly overwhelm community resources. During the 2013–2016 Ebola virus disease outbreak in West Africa, survivors developed often avoidable sight loss as a result of cataract, glaucoma and retinal detachment caused by intraocular inflammation. This outcome posed a new challenge for nongovernmental organizations, which were unprepared for this eventuality. Elsewhere, the measles outbreak in the Pacific region in 2019 triggered mass prophylactic treatment with vitamin A to prevent unnecessary sight loss from secondary ophthalmic complications.Local reconnaissance of disaster areas to estimate service gaps and injury types and numbers is essential to planning effective service delivery. Distinct patterns of injury can dominate depending on the nature of the incident, which may require a tailored response. For example, following the Nepal Earthquake of 2015, significant numbers of traumatic ocular injury were recorded, indicating a need for ophthalmic surgical teams.Effective eye trauma care is often time critical, and military units such as the United States of America Air Force Mobile Ophthalmic Surgery Team have the capacity to mount a response to emergencies at short notice.Providing ophthalmologists and ophthalmic personnel is essential to manage a variety of ophthalmic conditions that might become sight threatening if not addressed promptly. Inclusion of these specialists within emergency medical team responses allows the possibility of best outcomes for ophthalmic patients and reduces the workload of other emergency medicine physicians, general surgeons and allied professionals, allowing them to concentrate on their own skill sets.As the emergency relief phase settles, the team’s presence can be invaluable to prepare and contribute to the recovery phase of the affected country’s health system. Emergency medical teams that have invested in local infrastructure  are in a good position to contribute to the transition back to local services by supporting the management of chronic conditions (for instance cataracts and glaucoma) and mitigating the incidental increase in sight loss that might occur as a result of the disrupted service delivery.In some disasters, liaising with local health-care structures to coordinate an effective ophthalmic response might be possible. Eye camps, for example, are usually temporary screening and treatment centres that provide an effective platform for combating blindness in resource-poor settings. These centres could be strengthened with staff and equipment to form type 2 emergency medical team facilities in the event of a disaster. In addition, mobile eye units – ranging from well-equipped four-wheel drive utility vehicles to large specialist lorries with onboard operating theatres – might be deployed to locations cut off by natural disasters to offer essential ophthalmic services.Recent advances make it possible to use mobile phone applications to accurately record visual acuity and capture images of the retina. In addition, measurement of intraocular pressure can now be achieved easily using portable equipment. These advances, and the increased use of telemedicine, are revolutionizing eye care in resource-poor areas and could be harnessed by health-care workers to provide detailed information on ophthalmic conditions to centralized ophthalmic teams as part of the response to a disaster.We believe that awareness of the importance of eye care should inform the response to all sudden-onset disasters. To ensure basic eye care can be offered, we recommend a minimum proportion of emergency medical teams personnel to be trained to diagnose and manage several acute eye conditions using essential ophthalmic equipment and consumables. In addition, we recommend the use of specialist ophthalmology units able to deploy independently or attach to emergency medical teams. These specialist cells should consist of small surgical teams for rapid deployment, with the ability to use and boost local eye camps and mobile eye units, complementing current WHO emergency medical team structures .Combined with working operating theatres, these measures would help both in the initial management of ocular trauma and in returning normal service delivery to affected countries. Providing skilled ophthalmic care after a disaster may significantly facilitate the development of local eye health resources. Lessons can be learnt from efforts to rebuild the ophthalmic health-care system in post-conflict areas such as Timor-Leste, where international organizations worked with local government and services to optimize resources.Sudden-onset disasters expose populations to an increased risk of eye injuries and complications that can easily overwhelm local resources. Universal health coverage relies on global efforts to support damaged eye health systems. Integrating specialist ophthalmic services into emergency medical teams may be an effective way of managing ocular injuries, reducing unnecessary sight loss and supporting a transition back to local governance as infrastructure and human resources are rebuilt. Ocular health is an integral part of well-being, and ophthalmic care in the aftermath of a disaster should not be neglected.


The capacity of adult muscle to regenerate in response to injury stimuli represents an important homeostatic process. Regeneration is a highly coordinated program that partially recapitulates the embryonic developmental program and involves the activation of the muscle compartment of stem cells, namely satellite cells, as well as other precursor cells, whose activity is strictly dependent on environmental signals. However, muscle regeneration is severely compromised in several pathological conditions due to either the progressive loss of stem cell populations or to missing signals that limit the damaged tissues from efficiently activating a regenerative program. It is, therefore, plausible that the loss of control over these cells’ fate might lead to pathological cell differentiation, limiting the ability of a pathological muscle to sustain an efficient regenerative process. This Special Issue aims to bring together a collection of original research and review articles addressing the intriguing field of the cellular and molecular players involved in muscle homeostasis and regeneration and to suggest potential therapeutic approaches for degenerating muscle disease. The regeneration of adult tissues is a highly coordinated process that partially recapitulates the embryonic developmental program. The myogenic program involves an interplay between the intrinsic program of muscle lineage specification and extrinsic influence, such as innervation and growth factor activity. Along with a set of four key transcription factors, namely MyoD, Myf5, Myogenin, MRF4, other factors have been characterized that play a critical role during the different stages of muscle development and regeneration. The transcription factor NF-Y is an evolutionarily conserved heterotrimer formed by the sequence-specific NF-YA and the Histone Fold Domain—HFD—NF-YB/N. Libetti et al., analyzed the role of the two NF-YA isoforms in myogenic program, proposing that NF-YAl, but not NF-YAs, maintains muscle commitment by indirectly regulating Myogenin and MyoD expression in C2C12 cells . The chaThe life-long maintenance of muscle tissue has been the objective of numerous studies employing a variety of approaches. It is generally accepted that cumulative failure to repair damage related to an overall decrease in anabolic processes is a primary cause of functional impairment in muscle. Muscle regeneration occurs in five interrelated and time-dependent phases, namely degeneration (necrosis), inflammation, regeneration, remodeling, and maturation/functional repair . AlthougForcina et al., provided an exhaustive overview about the general aspects of muscle regeneration and discussed the different approaches to study the interrelated and time-dependent phases of muscle healing , whereasThe maintenance of an efficient regeneration process is guaranteed by both satellite cells’ niche environment and satellite cells’ pool. By disrupting either one of the two or both, the impairment in muscle regeneration suddenly happens, as likely occurs in many muscular dystrophies. Indeed, one of the critical points that remain to be addressed is why skeletal muscle fails to regenerate under pathological conditions. Either the resident muscle stem cells drastically decrease during aging and in several degenerative diseases or perhaps the pathological muscle is a prohibitive environment for stem cells activation and function. Although we lack definitive answers, several studies suggested that with age or under pathological conditions, the systemic environment impinges the activity of satellite cells and stimulates fibrotic accumulation .Muscle homeostasis and regeneration are indeed severely impinged in several pathologic conditions such as sarcopenia, cachexia, and muscle dystrophies.In the review by Pratt et al.  the authSatellite cells (SCs) are the main actors of myofiber regeneration after damage. Nevertheless, successful muscle healing requires the participation of additional cell types that directly or indirectly contribute to this process. SC activity is known to be influenced by signals deriving from the surrounding environment and by interactions with other cellular components of muscle niche. Petrilli et al., provided a comprehensive picture of the dynamics of the major cell populations that sensed and responded to acute damage in wild type mice and in a mouse model of Duchenne muscular dystrophy, a genetic disease characterized by impaired regenerative process and muscle wasting .One of the mechanisms by which cells communicate with each other involves a conserved delivery system based on the generation and release of extracellular vesicles (EVs). These vesicles transfer information between cells through several categories of cargo-enriched biomolecules , each of them selectively influencing different cellular domains. Picca et al., demonstrated that older adults with physical frailty and sarcopenia show increased levels of circulating small extracellular vesicles with a specific mitochondrial signature .Muscle wasting disorders, including sarcopenia and muscular dystrophies, are also characterized by the gradual replacement of muscle fibers by adipose and fibrotic tissue, which represent critical components of a hostile microenvironment/niche. Transforming Growth Factor β (TGF-β) is known for its role in the regulation of skeletal muscle size as well as fibrosis. Hillege et al.  assessedRecent studies on the factors involved in skeletal muscle growth, differentiation, homeostasis, and regeneration have provided new insights into the function of these signaling molecules in muscle and suggest promising new avenues for systemic as well as local intervention in the defects associated with many muscle pathologies. Thus, skeletal muscle repair/regeneration may benefit by the treatments with factors that favor regenerative myogenesis and support the robustness of regenerated myofibers.Nfix is mainly expressed by anti-inflammatory macrophages [Nfix in myeloid line displayed a significant defect in the process of muscle regeneration. Indeed, Nfix protein is involved in the macrophage phenotypical switch and macrophages lacking Nfix failed to adopt an anti-inflammatory phenotype and interact with myogenic cells.The inflammatory response of injured skeletal muscle plays an important and critical role in muscle homeostasis and regeneration and involves the recruitment of specific myeloid cell populations within the injured area. Thus, the inflammatory response is a coordinate process that must be finely regulated to obtain an efficient regenerative process, and the perturbed spatial distribution of inflammatory cells, altered identity of the inflammatory infiltrate (cell type and magnitude of influx) and disrupted temporal sequence results in a persistent rather than resolved inflammatory phase. The transcription factor Nfix, a member of the nuclear factor I (Nfi) family, plays a pivotal role during muscle development, regeneration and in the progression of muscular dystrophies. Saclier, et al., showed that rophages . Upon acCappellari et al., taking Duchenne muscular dystrophy as a paradigm of defect in muscle regeneration, reviewed the main effects of drugs on regeneration biomarkers to assess whether targeting pathogenic events can help to protect niche homeostasis and enhance regeneration efficiency other than protecting newly formed fibers from further damage . SqueccoThe study by Soendenbroe et al., supported the role of exercise in maintaining NMJ stability, even in elderly inactive individuals , whereasThe maintenance of skeletal muscle mass plays a critical role in health and quality of life. Jorgenson et al., discussed how mechanical loads are one of the most potent regulators of muscle mass . The autHomeostasis represents one of the most important and critical parameters of adult tissues, including skeletal muscle, and it is defined as the capability of a system to maintain a constant state of complexity and order in a dynamic equilibrium. Interestingly, skeletal muscle is well-maintained in hibernators during hibernation, a unique survival strategy exhibited by various mammals in order to cope with adverse environments in winter, during which hibernators not only face the challenge of prolonged skeletal muscle inactivity, but also deal with other stresses, including hypoxia, fasting, and repeated ischemia-reperfusion during the torpor-arousal cycle.2+) homeostasis is important for the preservation of a normal structure and function of skeletal muscle fibers. Skeletal muscle inactivity can trigger Ca2+ homeostasis disturbance, often characterized by cytoplasmic Ca2+ overload, leading to protein degradation and cell apoptosis, both involved in skeletal muscle loss. Zhang et al., demonstrated that under extreme conditions, such as low temperature, low metabolism, and prolonged hindlimb inactivity during hibernation, hibernating ground squirrels still possess the ability to maintain intracellular Ca2+ homeostasis [2+ homeostasis and avoiding skeletal muscle injury caused by its disturbance appear to be priority strategies employed by hibernating squirrels to cope with the various stresses induced during the torpor–arousal cycle. In addition, Malatesta et al., demonstrated that during hibernation, satellite cell nuclei maintain similar transcription and splicing activity as in euthermia, indicating an unmodified status during immobilization and hypometabolism [The maintenance of cytoplasmic calcium (Caeostasis . Therefotabolism . SkeletaWe believe that the papers in this Special Issue, each addressing a specific aspect of muscle homeostasis and regeneration under physiopathologic conditions, will help us to better understand the underlying mechanisms and will help to design more appropriate therapeutic approaches to improve muscle regeneration and to counteract muscle diseases.