Cytotoxic T lymphocytes (CTLs) play an important role in controlling tumours, and an improved understanding of how they accomplish this will benefit immunotherapeutic cancer treatment strategies. Stimulation of CTLs by targeting their CD137 receptor is a strategy currently under investigation for enhancing responses against tumours, yet so far only limited quantitative knowledge regarding the effects of such stimulation upon CTLs has been obtained. Here, we develop mathematical models to describe dynamic in vivo two-photon imaging of tumour infiltrating CTLs, to characterise differences in their function either in the presence or absence of a CD137 agonist antibody. We showed that an increased antiproliferative effect and a more sustained presence of CTLs within the tumour were the most important effects associated with anti-CD137 treatment.Several immunotherapeutic strategies for the treatment of cancer are under development. Two prominent strategies are adoptive cell transfer (ACT) of CTLs and modulation of CTL function with immune checkpoint inhibitors or with costimulatory antibodies. Despite some success with these approaches, there remains a lack of detailed and quantitative descriptions of the events following CTL transfer and the impact of immunomodulation. Here, we have applied ordinary differential equation models to two photon imaging data derived from a B16F10 murine melanoma. Models were parameterised with data from two different treatment conditions: either ACT-only, or ACT with intratumoural costimulation using a CD137 targeted antibody. Model dynamics and best fitting parameters were compared, in order to assess the mode of action of the CTLs and examine how the CD137 antibody influenced their activities. We found that the cytolytic activity of the transferred CTLs was minimal without CD137 costimulation, and that the CD137 targeted antibody did not enhance the per-capita killing ability of the transferred CTLs. Instead, the results of our modelling study suggest that an antiproliferative effect of CTLs exerted upon the tumour likely accounted for the majority of the reduction in tumour growth after CTL transfer. Moreover, we found that CD137 most likely improved tumour control via enhancement of this antiproliferative effect, as well as prolonging the period in which CTLs were inside the tumour, leading to a sustained duration of their antitumour effects following CD137 stimulation. The global immuno-oncology pipeline grew by 67% between 2017–2018 ,4,5. Alt+ T cells including enhanced proliferation and resistance to apoptosis [One immunomodulator which has received much recent attention is CD137: in 2017 there were nine trials with therapies targeting CD137; in 2018 there were 36 , the mecpoptosis ,13,14. AWe previously quantified the effect of CTLs on solid tumours, considering both cytotoxicity through direct cellular interactions ,15 and pThe experimental data we have used derives from a previously published study where mice were inoculated with B16F10 melanoma tumours, which were then studied over a period of up to 15 days post-inoculation through dorsal imaging windows, by means of two-photon microscopy . In totaThe dataset comprises estimates of tumour volume measured at days 1, 3, 6, 9, 13 and 15 in all mice. Moreover, for the mice treated with ACT on day 3 and bearing OVA expressing tumours, statistics were available for the number of CTLs and tumour cells, as well as their mitosis and apoptosis rates, on either day 6 or day 9 after tumour inoculation (days 3 and 6 after CTL transfer). These statistics are samples from imaging volumes of size 0.35 × 0.35 × 0.1 mm which were imaged for 1–3 h  in the absence of CTLs, based on growth rates for tumours in the data for conditions either without ACT or periods before CTL transfer in the ACT treated groups. This corresponds to a doubling time of 1.4 days for the tumour cell population, and it is approximately consistent with doubling times in the region 1.7–2 (day−1) reported for B16F10 tumours growing up to 100 mm3 in another study, where B16F10 cells were implanted into the ears of mice [The available tumour growth data derives from B16F10 murine melanoma tumours recorded for 15 days after their implantation . We opte applied ,19,20,22E, and the number of tumour cells, T, so the parameter −1 day−1). An advantage of this simplification is that the killing rate We consider the following model for the killing rate of tumour cells by CTLs:on model . Similaron model ,25,26, tur cells .T in Equation (2) is the total number of tumour cells in the tumour. Our quiescent state is motivated by observations of an IFNγ-dependent cell-cycle arrest in B16F10 melanoma after ACT [−1 day−1). CTLs secrete IFNγ upon encounter with antigen presenting cells [−1). The dynamics of proliferating and quiescent tumour cells can thus be described as in Equations (3) and (4):In our data the frequency of tumour cell apoptosis was low, so in addition to the killing we included an antiproliferative effect of CTLs on the tumour in our models. For this, we denote separately fter ACT , which wfter ACT . In our ng cells , therefofew days . In our Note that the killing rate is shared between proliferating and quiescent tumour cells in accordance with their fraction in the tumour.Our model considers only transferred CTLs (since only these could be seen with the fluorescent reporter system), therefore neglecting any endogenous response. Although endogenous CTLs may have been present, robust tumour control in the presence of ACT was still achieved in RAG −/− mice  for app3), therefore any distortion in tumour shape due to the presence of the imaging windows should be minimal. To take the CTL population dynamics into account, the second term of Equation (5) includes two additional variables I and R to describe the dynamics of CTL proliferation and apoptosis inside the tumour. The variable I represents an auto-inductive response of CTLs upon encounter with antigen expressing cells and is based on other models which have included Interleukin-2 as a driver of CTL mitosis [+ T cells by means of quorum regulation [+ T cells. Our model accounts for either possibility:The first term represents the net movement of transferred CTLs into the tumour. Since we have no measurements directly pertaining to CTL infiltration of the tumours, we opted for a simple model where the net movement of CTLs across the tumour boundary occurs at a constant rate pherical . We cons mitosis ,21. Suchgulation  or from E:T ratio, as the frequency of CTLs among tumour cells should determine the strength of the auto-inductive mechanisms.Thus, CTLs induce their own mitosis at rate us model  where thR represents a resistance acquired by the tumour in response to infiltrating CTLs, leading to an increase in the rate of CTL apoptosis over time. This variable rate of CTL apoptosis represents a difference between our model and previous models in the literature which assume a constant apoptosis rate for CTLs [The second variable for CTLs ,21,25. Ifor CTLs . HoweverE:T ratio inside the tumour at a constant rate I and R in terms of their effects on the mitosis or apoptosis rates of CTLs. Thus, I and R have units of events CTL−1 day−1 and additional scaling parameters in the second term of Equation (5) are unnecessary.Similar to the auto-inductive effect of CTLs, in Equation (7) CTLs induce resistance proportional to the T = 1200, E = 0, I = 0, R = 0. Since our model only describes the adoptively transferred CTLs, we set the infiltration parameter s = 0 until the day of CTL transfer (either day 3 or day 7). On the day of CTL transfer, the parameter s was immediately set to the best fitting value thus allowing CTLs to begin infiltrating the tumours.Models were initialized on day 0, corresponding to the day of tumour inoculation in the mice. The initial conditions were g) which was fixed to the same value for both ACT-only and ACT + mAb conditions before fitting. For each group we varied all the parameters to simultaneously minimise the errors for all available data points, for both day 3 and day 7 treated conditions together. This simultaneous fitting improves identifiability of the model by supplying additional constraints. For example, the intravital process dynamics are constrained by the requirement for the model to also describe the tumour volume progression (for which more time points are available). The intravital dynamics within the day 7 treated conditions (for which few measurements were available) are also constrained by the requirement for the model to describe the intravital dynamics within the day 3 treated tumours. Minimisation was performed using a differential evolution algorithm with the DEoptim [−1) for parameters ke, ki, kq, kr, di, and dr. For the parameter s we searched within bounds of 0–5 (TC−2/3 day−1). Each parameter estimation attempt was performed with 5 repeats, using different randomly selected starting parameter values for each repeat. Individual repeats had population sizes of 200 and ran for 500 generations.Models are fit by minimising the Root Mean Square Error between model prediction and all individual data points. “Individual data points” are considered to be either one volumetric growth estimate, or one statistic estimated from one intravital position. Thus, a single mouse where the tumour volume was measured on days 1, 3, 6, 9, 13 and 15 and where intravital data was recorded at four positions on days 6 and 9 would produce 5 volumetric growth estimates, plus 8 estimates for each intravital process rate  and a further 8 E:T ratio estimates. The correspondence between the intravital process rates determined from the experimental dataset and those determined from the ODE model is given in  DEoptim  package In this work our aim was to integrate dynamic two-photon imaging and volumetric tumour progression data , to creaBesides mitosis and apoptosis rates A,B, the The number of either CTLs or TCs per position was similar between ACT-only and ACT + mAb groups, although CTL numbers were slightly higher in ACT + mAb conditions E. This r−1). We estimated that the growth rate of the untreated tumours was approximately 0.5 day−1  ratio to develop our ODE model. A further advantage of utilising the dimensionless E:T ratio for fitting is that it prevents the physical size of the imaging windows unduly influencing our results. Our ODE model (E) either killing tumour cells (T) or preventing them from proliferating, which in our model happens via transfer of proliferating (pT) tumour cells into a quiescent state (qT). To describe the population dynamics of CTLs we considered CTLs to infiltrate across the tumour boundary at a constant rate s per unit area of boundary. Additionally, since CTL mitosis and apoptosis measurements within the tumour were available we included these processes in our model as well via incorporation of two loops. The first loop considered a factor stimulating CTL proliferation (Induction/Interleukin: I), whereas the second loop described a tumour resistance factor (R). The resistance factor increased over time spent with CTLs inside the tumour and led to an increase in CTL apoptosis (see −1). Overall we varied 7 parameters for either the ACT-only or the ACT+mAb condition (In order to probe the relative contributions of these two effects (antimitotic or killing) we elected to develop an ODE model to combine all the disparate measurements together and check them for internal consistency and with other reports in the literature. The absolute number of tumour cells per field in the imaging data depended strongly on the location of the imaging windows which were sometimes located in the centre of the tumour but other times near the periphery or contained large features like vessels A. TherefDE model B featureosis see . We fit ondition  to obtaiondition . Indeed,ondition . In conce, abrogation of killing  could be of greater therapeutic benefit than strategies aiming to further enhance the antimitotic effect (kq).We studied our best fitting models to gain insight into the dynamics and activities of the transferred CTLs. When we took best fitting parameter sets for either ACT-only or ACT+mAb conditions and varied the killing rate k kq > 1; B. Note temployed . Neverthe = 0 had no substantial impact on tumour volume progression (e = 0.75 day−1) were even somewhat higher than for the ACT+mAb condition (ke = 0.5 day−1)  was substantially larger for the ACT-only condition than for the ACT+mAb condition  was negative in almost all videos, the overall dynamics of the anti-CD137 stimulated CTL populations appeared different to their unstimulated counterparts. Specifically, the peak rate of CTL mitosis occurred later in the ACT+mAb treated group, which together with a delayed onset of apoptosis led to a more sustained presence in the tumour. In summary, after anti-CD137 treatment CTLs were able to rapidly shut down mitosis of tumour cells, but also remained present in the tumour for longer, both contributing to the improvement in tumour control.Importantly, we also sought to understand mechanistically what caused the improved tumour control after treatment with anti-CD137. Killing per CTL was unchanged after anti-CD137 stimulation and hence still contributed little to tumour control, so the improvement in tumour control was largely due to an enhanced antimitotic effect after anti-CD137 treatment. Our model suggested that this enhanced antimitotic effect could be explained either by an increased per-capita ability of CTLs to exert an antimitotic effect, or simply by a numeric increase of CTLs inside the tumour (each with similar antimitotic effects to unstimulated CTLs when considered per-capita). In our model these effects were difficult to separate, since increased mitotic effect of CTLs (on a per-capita basis) should also have the effect of increasing Our results fit well with other reports about the effects of anti-CD137 stimulatory effects on CTL function in the literature. To our knowledge, there are no clear reports that CD137 enhances the cytotoxicity of CTLs. Instead, in agreement with our findings, studies which have directly measured CTL cytolytic activity have found similar killing after blockade of CD137 signalling , and CAROne limitation of our model is that there is no representation of space, so the tumour is treated as homogeneous throughout. The B16F10 melanoma tumours are highly invasive  and evenOur model predictions can be tested in various ways. Although we included the tumours treated on day 7 in our analysis, we were not able to determine the number of TC apoptosis events or the apoptosis/mitosis of CTLs. That was because, whilst absolute cell numbers or TC mitosis events were relatively easy to detect, the other events were ambiguous. Nevertheless, our model does make predictions for these values which could in principle be checked. Moreover, our model predicts the arrival rate of new CTLs into the tumours which could be checked against time-lapse images. In our data CTL apoptosis increased over time, so we introduced a resistance variable R to account for this. This is consistent with recent reports that long range IFN-γ signalling can cause upregulation of PD-L1 in cells across distances of up to 800 μm ; or thatIn future, our modelling approach could be extended to include human data. For such an extension, it is a prerequisite that the model should be defined in terms of variables and parameters that can feasibly be measured in human patients. For certain variables this is already possible, for example the frequency of tumour infiltrating CTLs can be obtained from tumour biopsies using immunofluorescence techniques  or from Overall, our modelling study provides insights into the mechanisms CTLs use to control tumours, as well as insights into how these mechanisms may have changed upon costimulation with agonist antibody targeting the CD137 receptor. We found that the cytolytic activity of transferred CTLs against tumour cells was low, with an antiproliferative effect exerted upon tumour cells being a more important factor in tumour control. Moreover, our model suggested that a stronger antiproliferative effect together with a more sustained presence of CTLs inside the tumour were the main effects of costimulation via the CD137 receptor, which together led to improved tumour control. The results of our model identify specific directions for future experimental work which would help elucidate the effect of CD137 stimulation upon CTLs.


In 2012, the Korean National Health Insurance extended its coverage to include denture services for older adults. We examined whether the new policy resulted in improved chewing ability in the eligible population.We used interrupted time-series (ITS) analysis, a quasi-experimental design, to analyze the effect of the policy. We used data from the Korea National Health and Nutrition Examination Survey conducted from 2007 to 2016–2018. The study population consisted of two groups: the treatment group, aged 65 years or older and eligible for the dental insurance benefit; and the control group, those younger than 65 years and ineligible. The main evaluated outcome was self-reported chewing difficulty.The ITS analysis showed that chewing difficulty decreased annually by 0.93%  and 0.38%  after the policy extension in the older than 65 and younger than 65 groups, respectively. However, we could not conclude that the insurance extension affected chewing difficulty because there was a decrease in the control group as well.Chewing ability improved in both older and younger adults regardless of dental insurance coverage for older adults. Other exogenous factors probably led to the improvements in chewing ability as well as dental insurance benefits. Globally, poor older people are more likely to have serious oral problems due to economic barriers to dental care, resulting in higher burdens of disease.South Korea launched a dental insurance expansion for older adults in 2012 under their national health care plan. Individuals aged 65 years and older became eligible for elderly dental insurance benefits, resulting in savings of approximately 50–70% for out-of-pocket expenses related to dentures and dental implants. Dental scaling has been covered for all adults since 2013, encouraging access to preventive dental care for those populations.6–8 In addition, expanded dental insurance for older adults was shown to alleviate the cost burden of treatment in this age group.9 On the other hand, significant out-of-pocket cost payments remain for low income older adults.10 Studies have also indicated inconsistent findings regarding the impact of insurance expansion on socioeconomic inequalities in oral health.7,11,12 Dental care access improved for higher socioeconomic groups after the insurance expansion,11,12 resulting in persistent oral health inequalities on the relative scale .7However, mixed results have been reported with regard to dental insurance benefits. For example, in the South Korean context, insurance expansion was shown to improve dental visits and self-reported oral health.However, the findings of previous studies were not based on a strong causal identification strategy. For example, they did not attempt to address the endogeneity of extended dental insurance  by estimating the effects of coverage changes.13,14In the present study, we sought to implement a quasi-experimental approach by evaluating the effects of a policy change that was plausibly exogenous, or unrelated to health and to all observed or unobserved predictors of both coverage and health outcomes.15,16 However, these findings were made in the immediate aftermath of the policy change, and any insurance effects on oral health outcomes in older adults need to be considered in the long-term.Focusing on older adults, there have been two studies that implemented a quasi-experimental regression discontinuity design to examine the effect of insurance expansion on oral health outcomes. Despite cost sharing for dentures and an increased denture-usage rate after insurance expansion, there has not been strong evidence of improvement in chewing ability.Thus, we sought to adopt interrupted time-series analysis (ITSA) models to consider population changes in chewing difficulty over time. ITSA uses multiple consecutive pre- and post-intervention observations in a single population and incorporates time values.Given these assumptions, we aimed to explore the causal inference of expanded dental insurance on older adults’ chewing ability using a multi-group ITSA model.We applied a multi-group ITSA, quasi-experimental study design to analyze the Korea National Health and Nutrition Examination Survey (KNHANES) data from 2007 to 2016–2018. The KNHANES is a nationally representative, cross-sectional survey conducted by the Korea Centers for Disease Control and Prevention (KCDC). We performed pre-intervention (2007 to 2011) and post-intervention (2012 to 2016–2018) comparisons.17The 2016–2018 data were collected at a single point that was provided by the KCDC, which could not identify each year’s data because of the small number of sample participants. For instance, in the 2011 data, 192 primary sampling units (PSUs) were drawn from approximately 200,000 geographically defined PSUs to consider urban and rural areas for the whole country, which comprised an average of 60 households, and of these, 20 final target households were sampled for each PSU using systematic sampling that includes approximately 10,000 individuals aged 1 year and over for each survey year.The study population comprised treatment and control groups. The treatment group was aged 65 years or older (aged 65 to 80 years); that is, those eligible for the elderly dental insurance benefit. The control group was aged younger than 65 years (aged 50 to 64 years); that is, those ineligible for the elderly dental insurance benefit. We restricted the sample to adults aged between 50 and 80 years to compare the same range of ages 65 ± 15 years) in both groups. Approximately 3,000 individuals were included for each year  as chewing difficulty. Self-reported chewing ability is a valid and reliable measurement to evaluate mastication function for clinical tests and population studies among adults and older adults.We performed a multi-group ITSA. Stratified analyses were performed to establish whether the policy effect on chewing difficulty varied by gender.newey” Stata command, which estimates the coefficients to handle autocorrelation and possible heteroskedasticity. We implemented post-estimation time-series analysis to verify the post-intervention trend using the “itsa” Stata command specifically designed for time-series data.21Ordinary least-squares (OLS) regression models designed to adjust for autocorrelation were employed for the multi-group ITSA, which included the treatment and control groups, testing our hypothesis that confounding omitted variables affect both groups similarly. We employed the “Yt is the chewing difficulty rate measured for each year t, Tt is the number of years since the start of the study, Xt is a dummy variable representing the intervention , Z is a dummy variable that denotes group assignment (1 = treatment), and XtTt, ZTt, ZXt, and ZXtTt are all interaction terms of the previously described variables. These terms are illustrated in Figure The multi-group analysis is expressed as follows:21The coefficients of the lower blue dashed line, β0 to β3, represent the control group, and the coefficients of the upper red bold line, β4 to β7, represent the treatment group. More specifically, β4 represents the difference in the level (intercept) of chewing difficulty between the treatment and control groups prior to the intervention, β5 represents the difference in the chewing difficulty slope (trend) between the treatment and control prior to the intervention, β6 indicates the difference in the chewing difficulty level between the treatment and control immediately following the intervention introduction, and β7 represents the difference in the chewing difficulty slope (trend) between the treatment and control after the policy initiation compared with the pre-intervention slope.21We conducted sensitivity tests to verify the ITSA results’ robustness. These included an alternative control group (aged 20–35 years). We tested for autocorrelation to ensure model fit that accounts for the correct autocorrelation structure in the error distribution with a Cumby-Huizinga general test using the “actest” Stata command.We used Stata statistical software  for all statistical analyses.This study used open-access data from the KNHANES conducted by the KCDC. All KNHANES respondents provided informed consent before participating. The KNHANES was approved by the KCDC Institutional Review Board (IRB). This is a publicly available secondary dataset. Our institute determined that the use of the KNHANES dataset does not meet the criteria for human subject research and is therefore exempt from IRB approval. It confirmed that all methods meet the relevant guidelines and regulations.The trend in chewing difficulty slopes downward over time in both those aged 65 years or older and those younger than 65 years after 2012. Figure Table Stratifying the findings by gender shows some different features in the post-trend estimation; β1 + β3 + β5 + β7 and β5 + β7. We found that chewing difficulty decreases significantly in both genders annually after the intervention; women  and men . However, compared with the control group (β5 + β7), these differences decrease more in women  but not in men   of the ITSA models . Thus, wThe major findings can be summarized as follows; first, the trend in chewing ability improved for older adults after the dental insurance policy. Second, however, we could not conclude that this improvement was due to the expansion in dental insurance because of a parallel improvement of chewing ability in both the eligible and ineligible groups. Finally, we found that chewing ability tended to improve in both older and younger adults regardless of the expansion in dental insurance coverage for older adults.22 We employed OLS regression to ensure the robustness of our estimates with adjusting for autocorrelation.21,23 The slope change prior to the intervention indicates that an exogenous factor was already influencing the chewing difficulty rates and implies that the actual intervention  may simply be a continuation of those trends.24Different modeling techniques can provide different treatment effects.P-values > 0.1 for both). The two parameters play a vital role in establishing whether the treatment group and controls are balanced on both the level and trend of the chewing difficulty in the pre-intervention period. We would expect similar levels and trends prior to the intervention if the difference in the mean baseline slope (β5) is significant and if that level remains the same throughout the duration of the observation period  and slopes (β5) comparing the treatment and control groups suggest that the groups were not significantly different with respect to pre-intervention levels of chewing difficulty and trends  for chewing ability. However, previous studies found that women report more chewing difficulty than men among older adults.nd older . Compare28,29 The severity of periodontal symptoms, including tooth mobility, gingival swelling, and gingival bleeding, decreased after 2012.8 Self-reported oral health also improved in the same period.7 These improved oral health outcomes were found in adults aged 65 years and older as well as those younger than 65 years.7,8,28,29 These trends suggest reasons for the improvement in chewing ability in both age groups and why it might not be attributable to the health policy change. This would seem to imply that, besides the changes in older adults’ dental insurance benefits, there have been other influences on improving oral health after 2012; for example, insurance extended coverage of dental scaling services for everyone over 19 years from 2013, which could affect the improvements in both groups.6,11,30 This could have led to improved dental care and oral health outcomes.Enhanced chewing ability can be explained by improved oral health outcomes that relate to chewing functions . Recent evidence supports this .31 Likewise there are influences beyond dental treatment that affect chewing ability. Important gaps remain in the evidence linking long-term effects of health policies to oral health. For example, further study is needed to probe whether trends in non-health care-related factors—such as food consumption practices or other social determinants—affect trends in oral health outcomes.A recent study suggested that dental prosthesis may not always lead to positive outcomes such as chewing ability and oral health-related quality of life.7,16 Self-reported outcomes might cause bias. However, such a bias would affect both groups during the study periods. Thus, we thought that the internal validity of our results would not be affected.Some limitations in the ITSA model should be reported. Our results might have derived from exogenous factors beyond the expansion of dental insurance. Furthermore, we did not consider the actual uptake of dental insurance benefits. However, we did not present the results to confirm the assumption of compositional stability of the participants over time in this study, though we checked that the distributions of the participants remained stable on the factors linked to oral health outcomes.Chewing difficulty decreased over time in both the eligible and ineligible groups after the expansion of dental insurance in South Korea. These trends appear to be attributable to forces external to changes in dental insurance benefits.


For GI-Sim, the absolute average fold error (AAFE) valuesfor AUCt0- and Frel,colon were within a 2-fold prediction error for bothsolutions  and suspensions . For GastroPlus, the AAFE values for AUCt0- and Frel,colon wereoutside the set 2-fold prediction error limit for accurate predictionsfor both solutions  and suspensions . No trends for over- or underprediction wereobserved for GI-Sim, whereas GastroPlus showed a slight trend forunderprediction of both AUCt0- and Frel,colon for compounds with low permeability.In addition, regional differences in the plasma profiles were qualitativelypredicted in the majority of cases for both software. Despite thedifferences in prediction performance, both models can be consideredto predict regional differences in absorption as well as AUCt0- and Frel,colon withacceptable accuracy in an early development setting. The results ofthis study indicate that it is acceptable to replace in vivo regionalabsorption studies in dogs with the evaluated models as a method forthe early assessment of the risk for colon absorption limitation ofMR drug product candidates.Colon absorptionis a key determinant for the successful developmentof modified-release (MR) formulations, and the risk that colon absorptionmay limit the in vivo performance of an MR product can be assessedearly by various in vitro tests or by preclinical in vivo regionalabsorption studies in dogs. Mechanistic physiologically based biopharmaceuticsmodeling (PBBM) is becoming increasingly accepted to predict in vivoperformance and guide formulation development; however, no evaluationof the ability to predict colon absorption has been performed. Thepurpose of this study was to investigate if regional and colon absorptionof drugs in dogs could be predicted with sufficient accuracy usingPBBM to enable the replacement of in vivo dog studies in the earlyassessment of colon absorption limitation risks. This was done bypredicting the regional and colon absorption and plasma exposure of14 drugs after administration to the dog colon according to an a prioriapproach using the in silico absorption models GI-Sim and GastroPlus.Predictive performance was primarily assessed by comparing observedand predicted plasma concentration–time profiles, AUC Consequently,it is important to understand the colon absorption for such drug candidates.2 As the colon is structurally and anatomicallydifferent from the SI, it provides additional barriers against drugabsorption. Differences in permeability between the SI and the colondue to smaller surface area and tighter junctions in the epithelialcell layer have been reported and differences in transporter expressionlevels may also result in regional permeability differences.5 Furthermore, factors including lower water content, irregular motility,viscosity, and lack of bile salts are believed to restrict solubilityand dissolution in the colon.7 The distribution ofdrug-metabolizing enzymes has also been reported to vary between regionsand drugs may be subject to bacteria-mediated degradation in the colon.10 It is of great importance to understand the impact of regional differencesin intestinal absorption as well as to be able to predict the extentof absorption from the colon and consequently the in vivo performanceof MR products.2Gastrointestinal(GI) absorption is one of the key factors determiningthe in vivo performance of orally administered drugs. GI permeabilityand solubility of the drug as well as the dissolution/release ratefrom the formulation are the main determinants of the fraction ofthe dose absorbed.14 Usually these studies are performed before initiating MR productdevelopment, but ideally the development risks associated with limitedcolon absorption should be assessed early during the candidate selectionor preclinical development phases. Recently, in vivo predictive invitro methods such as in vitro permeability assays, simulated biorelevantcolon media for solubility/dissolution investigations as well as colonstability assays have emerged as tools for the early assessment ofthe potential for absorption in the colon.17 In addition, it has been demonstratedthat dog colonoscopy and colon stoma models can be predictive of humancolon absorption and permeability, and as a result, the dog is currentlythe main preclinical model for the assessment of colon absorptionlimitation risks.21The extent of colonabsorption in humans may be assessed directlyby human regional relative bioavailability studies using intubation,capsule techniques, and colonoscopy techniques.22 has become increasingly acceptable for predictions of the rate andextent of absorption. There are several software packages availablefor the prediction of intestinal absorption such as GastroPlus, Simcyp,PK-Sim, and GI-Sim.26 These models integrate anatomical and physiological parameters,physicochemical properties of the active pharmaceutical ingredientas well as formulation properties to predict the in vivo performanceof a drug.27 The models have the advantageof being able to incorporate all aspects of importance for absorptionthus enabling a potential comprehensive assessment of a drug candidate.There are several cases where absorption modeling has also been provenuseful to guide MR formulation development.29 Furthermore, in silico models of preclinicalspecies have been used to improve the confidence in predictions ofhuman regional absorption.30 To successfullyapply these models in drug development in the absence of any measuredin vivo data, the ability of the in silico models to adequately predictin vivo performance should first be evaluated. Recently, evaluationsof the predictive performance of several available models with respectto absorption mainly in the SI have been published.32 However, the need for improved colon modelshas been identified and an in-depth evaluation of the predictive powerregarding colon absorption has not been published.33Despite recent advancements,the colon absorption assessment capabilitycould be further improved. In vivo studies are costly and time consuming,and in addition, there are ethical aspects to consider for animalin vivo studies, where the aim should be to remove or replace suchstudies with other methodologies when possible. In addition, the availablein vitro methods all have the limitation that they only measure oneparameter in isolation. Recently, the application of mechanistic physiologicallybased biopharmaceutics modeling (PBBM)The main purpose of this study wasto investigate the ability ofGI-Sim and GastroPlus to predict the regional and colon absorptionof drugs in dogs to evaluate if PBBM approaches could be used to replacedog in vivo studies in the early assessment of colon absorption limitationrisks. This would in turn reduce the use of animals and enable a moretime and cost-efficient MR product development.22.1fabs), the relative colon bioavailability , and plasma pharmacokinetic (PK) parameters,primarily area under the plasma concentration–time curve (AUC),for a set of model drugs, which have been administered both orally(or to the duodenum) and directly to the colon in dogs. The studyincluded simulations of 14 compounds, administered as solutions and/orsuspensions. The absorption modeling was performed according to ana priori approach where no fitting to observations was allowed, whilethe systemic PK input parameters were obtained by compartmental modelingof intravenous data. An effort was taken to harmonize the input parametersbetween the different software. In vivo data from different dog breedswere used, including data from Beagle, Labrador, and Mongrel dogs.The predictiveperformance of the dog colon models in GI-Sim and GastroPlus wereevaluated through predictions of fraction absorbed  and GI-Sim (version 5.2). They both employ a series of coupledcompartments as a model of the GI tract.ling see 2.2.1.2.2.126 The fasted Beagle physiology in GI-Sim consistsof nine compartments: stomach (1), duodenum (2), jejunum 1 (3), jejunum2 (4), ileum 1 (5), ileum 2 (6), ileum 3 (7), ileum 4 (8), and colon(9). For the purpose of this study, the surface area in the coloncompartment in the dog model was derived from the GI-Sim human fastedmodel. In the human model, the colonic surface area (including thececum) constitutes 3.5% of the total surface area in the GI tract.Assuming that the same is true for the dog, a colon surface area of17 cm2 was estimated. This area was not intended to reflectthe true physiological area of the dog colon but rather an initialestimate of the area available for absorption. The full physiologicalmodel, including the updated surface area, is described in 31 Since the dose-to-colon optioncurrently does not allow entry of particle size distribution data,only mean particle radius was used as input in the GI-Sim predictions.GI-Simis a mechanisticphysiologically based absorption model, which has been internallydeveloped at AstraZeneca and has been thoroughly described elsewhere.2.2.223 The “immediaterelease solution” or “immediate release suspension”dosing options were used for solutions and suspensions, respectively.For AZ1, particle size was described by fitting a distribution curveonto the full particle size distribution using 10 particle size bins.For all other compounds, mean particle size was used as input. Thefasted Beagle physiology in GastroPlus is made up of nine compartments:stomach (1), duodenum (2), jejunum 1 (3), jejunum 2 (4), ileum 1 (5),ileum 2 (6), ileum 3 (7), cecum (8), and ascending colon (9). Thephysiology is summarized in GastroPlus  is based on the advanced compartmentalabsorption and transit (ACAT) model and has previously been describedby Agoram et al.2.35 When intravenous and oral/colon data for a specificcompound were obtained from different dog breeds, the PK parameters were normalized againstbody weight to reflect the correct breed in the predictions of exposureafter oral/colon administrations. The same PK parameters were usedas input in GI-Sim to avoid potential differences in PK algorithms.The first-pass liver extraction was estimated byEH is thehepatic extraction ratio, CLH the hepatic clearance, QH the hepatic blood flow (39.6 L/h for a 12kg dog), and B/P is the blood:plasma concentration ratio . CLH was assumed to be equal to nonrenalclearance and was calculated by CL = CLH + CLR. CLR was estimated by fu × GFR, where fu is thefraction unbound and GFR is the glomerular filtration rate, whichwas assumed to be 61.3 mL/min for a 12 kg dog. Where no values forfu were available , CLR wasassumed to be zero. For metoprolol, the estimated CLR usingthe abovementioned strategy generated first-pass values above 100%.For this reason, QH was normalized againstthe weight of 30 kg dog, and this value (99 L/h) was used to estimatethe first-pass extraction of metoprolol. For theophylline, the calculatedCLR was higher than the total CL generated by compartmentalmodeling of the available in vivo data, and in this case, CLR was set at zero for the purpose of the simulations. Biopharmaceuticsand physicochemical properties of the drugs were gathered from previouslypublished reports or internal measurements at AstraZeneca (31 Molar density (ρ) was calculatedby ρ =MW/VM, where MW is themolecular weight and VM is the molar volume.The diffusion coefficient in water (D) was estimatedby Stoke–Einstein’s equationk is the Boltzmann’sconstant, T is the absolute temperature, ηis the viscosity of water, and r is the moleculeradius. Missing data on ρ or D were handledusing default values of 1.2 g/mL and 0.76 × 10–9 m2/s, respectively, as previously described.31 The effective permeability (Peff) indogs was estimated as described below.The selection of model drugs in this investigationwas based onthe availability of in vivo data after administration directly tothe colon in dogs. An effort was made to include a broad range ofcompounds, covering all four BCS classes. Systemic PK parameters wereestimated by compartmental modeling of the plasma profiles after intravenousadministration using the PK Plus module in GastroPlus 3. All plraZeneca 4. In vit2.3.1Peff values are rarely available and there is no well-established correlationbetween dog and human Peff, three differentstrategies were evaluated to estimate Peff in dog in this study:1.Peff is the same in dogs and humans for all compounds.The first approach assumed that 2.Peff in dogs is approximately 3 times higher than the human Peff. In GastroPlus, the dog Peff is approximately 2.4–3-fold higher than thehuman Peff, depending on the permeabilityinput value. Therefore, approach 2 was considered to be representablefor the default settings in GastroPlus.The second approach used the correlationincorporated in GI-Sim, which assumes that 3.5 Their results indicate that Peff is higher in dog for low-permeability compounds, butthat Peff is similar in dogs and humansfor high-permeability compounds.5 In thisevaluation, a limit was set at a human Peff of 1.34 (human Peff of the high-permeabilitymarker metoprolol). Compounds with a Peff lower or equal to 1.34 were assumed to follow the GI-Sim correlationof having a 3-fold higher permeability in dogs. Compounds with a Peff above 1.34 were assumed to have the same Peff in dogs and humans.The third strategy divided the compoundsinto two groups based on previous work by Dahlgren et al.Since dog intestinal Peff values were used as a basis for all three approaches.Where no Peff values were available, apparentpermeability (Papp) in Caco-2 cell lineswere used to predict human Peff accordingto a previously established Caco-2 Papp–human Peff correlation. The differentapproaches were evaluated in initial simulations of 15 compounds afteroral and colon administrations in GI-Sim only. A strategy was chosenbased on the ability to predict area under the plasma concentration–timecurve up to the last measured concentration (AUCt0-), peak plasma concentration (Cmax), and time to peak plasma concentration (tmax) and was used for the full evaluation in both software.Previously measured or estimated human 2.4t0- and the relativebioavailability after administration to the colon  in comparison to oral/duodenal administration(AUCcolon/AUCref). The predicted fraction absorbedin the colon  was also notedfor each simulation. The absolute average fold error (AAFE) was usedas a measure of the overall predictive accuracy.34 Furthermore, the percentage of the predictions within 2-foldof the observations were documented.The evaluation of the ability of the models to predict the extentof absorption in the colon was primarily based on the ability to predictthe mean AUCPeff, solubility, or predicted fabs, colon.Results were examined todiscover any trends in the predictive performance depending on 33.1Peff in dogs, thestrategy which divided the compounds into two different groups accordingto permeability class was found to be somewhat better than the otherapproaches and was selected for estimation of dog Peff throughout the remainder of the study . The strategy assuming dog Peff = human Peff resulted in a tendency for underprediction of AUC , whereas the strategy assuming3-fold higher Peff in dogs compared tohumans regardless of permeability class resulted in a tendency foroverprediction .The estimated dog Peff values used inthe final simulations are summarized in Out of the three evaluatedstrategies to estimate 3.2t0-, and Frel,colon afteradministration to the colon in dog, but also with regards to Cmax and tmax. Thirteenof the 14 model drugs were administered to the colon as a solution,while colon absorption data for suspensions were available for sixof the model drugs. The observed and predicted plasma concentration–timeprofiles after oral/duodenal and colon administration are shown in GI-Sim and GastroPlus were primarily evaluatedwith respect to their ability to predict AUCt0- and Frel,colon wereboth within a 2-fold prediction error and there was no trend for over-/underprediction with correspondingAFE values of 1.04 and 1.10, respectively , but the corresponding AFE values of 0.64and 0.77 indicated a trend for underprediction  and the corresponding AFE values were 0.54 and 0.53,which indicated a trend for underprediction  is taken into account when the Peff in each compartment is calculated while GI-Sim onlyconsiders the unionized fraction.26 Changes inthe colon absorption scale factors may be considered to improve theprediction accuracy for low-permeability drugs in GastroPlus, butsuch an evaluation was out of scope for this study. Furthermore, bothGastroPlus and GI-Sim overpredicted the colon absorption for the solutionsof the poorly soluble drugs AZ1, AZ3, and felodipine, which couldbe due to the fact that precipitation may have occurred in vivo asdescribed earlier by Sutton.20 If suchinformation would have been available and accounted for in the modeling,the observed prediction performance might have been improved for bothsoftware.The extent of colon absorption of solutions wasconsidered to bepredicted with a sufficient degree of accuracy by GI-Sim since thepredefined limit for accurate predictions (AAFE ≤ 2) was metand since no trend for over-/underprediction was observed. In addition,the predictive performance was not dependent on the predicted eff usedA,C. For t0- and Frel,colon, but with a slight trend for underprediction.The low number of compounds administered as a suspension made it moredifficult to detect any clear trends, but GI-Sim may potentially underpredictboth AUCt0- and Frel,colon of low-solubility compounds (high dose/solubilityratios). For GastroPlus, the AAFE values for AUCt0- and Frel,colon were2.94 and 2.09, respectively, and both parameters were generally overpredicted.Part of the reason for these values was the large overprediction ofthe extent of colon absorption of aprepitant. This compound differedfrom the others as it was administered as a nanosuspension, whichis more complex to model. Aprepitant was better predicted by GI-Sim,which is in line with previous studies demonstrating the ability ofGI-Sim to predict increases in absorption and exposure achieved withnanoformulations of poorly soluble drugs.26 Furthermore, the prediction accuracy of GastroPlus did not seemto be dependent on the dose/solubility ratio. Some additional considerationsshould be taken into account regarding the prediction accuracy forthe suspensions. In some cases, Frel,colon of the suspension was calculated using data for an oral solutionas reference, which does not accurately reflect the difference ofa suspension administered orally as compared to colon. Second, thecompounds administered as suspensions in this study were all low-solubilitycompounds, making modeling of the dissolution process particularlychallenging46The extent of colon absorption of suspensions was considered tobe predicted with a sufficient degree of accuracy by GI-Sim sincethe predefined limit for accurate predictions (AAFE ≤ 2) wasmet for both AUCt0- and Frel,colon with acceptable accuracy in the majority ofcases. It should also be taken into consideration that in this evaluation,the intention was to make the simulation conditions as similar aspossible in both software. The applied methodology may not be optimalfor any of the investigated software but reflects the effort to generatecomparable results. With all of this in mind, the results suggestthat it may indeed be possible to replace in vivo regional absorptionstudies in dogs in the early assessment of the risk for colon absorptionlimitation with the evaluated models.In an early risk assessmentsetting, the main purpose is to beable to predict potential limitations in colon absorption. Hence,even where a quantitatively accurate prediction of exposure afteradministration to the colon is not achieved, the ability to qualitativelypredict differences in regional absorption should be considered enoughto enable this risk assessment. Although there were some differencesin the prediction performance between GI-Sim and GastroPlus, wherethe AAFE criteria were not met by GastroPlus, overall both modelswere able to predict regional differences in absorption as well asthe AUCPeff values have been published for some of the compounds includedin this study,5 this is generally not thecase. Both GI-Sim and GastroPlus have built-in human Peff–dog Peff correlations,but the accuracy of the available correlations is not well-established.Therefore, in this study, three general approaches to estimate dog Peff were evaluated and the approach, dividingthe compounds into two groups depending on the human permeabilityclass, was the most successful. The defined limit of a human Peff of 1.34 (Peff for metoprolol) was based on a work by Dahlgren et al., where theymeasured Peff indirectly in dogs withintestinal stomas and presented data showing a higher permeabilityin dogs in comparison to humans for the low-permeability compoundatenolol, whereas the high-permeability compounds metoprolol and ketoprofenhad similar Peff values in dogs and human.5 Although the exact limit is somewhat arbitrary,one could argue that, out of the approaches examined here, this isthe most scientifically sound approach based on available data. Consideringphysiological differences in the GI tract, it is plausible that compoundswith low permeability in humans may be better absorbed in dogs dueto increased possibilities for paracellular transport.49 However, when passive transcellular permeability is already sufficientlyhigh in humans, the larger paracellular pores in the dogs play a minorquantitative role. Overall, it was concluded that, since this approachoffered the best predictive performance and was considered mechanisticallysound, it was used to estimate dog Peff throughout this study.One critical step in themodeling strategy was the selection ofpermeability value in dogs. Even though dog Peff discussed above, the physiological relevance couldbe increased. For example, the scaling of the surface area availablefor absorption in the colon in GI-Sim should ideally be derived fromthe understanding of the dog colon physiology rather than scaled fromthe human model. The dog colon is known to be substantially shorterthan the human colon and a direct adaption from the human model mightnot be appropriate.50 The SI part of theGI-Sim dog model could also be modified to more accurately reflectthe physiology of the dog GI tract. It has been proposed that a moreappropriate model should have a larger number of jejunal compartmentsto reflect the fact that dogs have a proportionally longer jejunumand shorter ileum than humans.52 However, this was outof scope for this study.Despite the encouraging results obtainedin this study, the predictiveperformance of GI-Sim and GastroPlus could be further improved. Inaddition to improving the estimation of the dog 53 Furthermore,data on mean particle size was lacking in some cases and full particlesize distribution data was only available for AZ1. It is possiblethat more accurate predictions could have been obtained for some ofthe suspensions if this data had been available. Finally, it shouldbe pointed out that the built-in human–dog Peff conversion in GastroPlus was not tested in this study,but this is anticipated to have no or minor effects on the obtainedresults. An in-depth evaluation of the reasons for any differencein the prediction performance between the different models was beyondthe scope of this evaluation.In this evaluation, care was takento ensure the use of high-qualityinput data when available but since data was gathered from many differentsources there is a significant source of variability in how the datawas generated. Additionally, data was gathered from different dogbreeds, but all simulations were performed using a Beagle model, whichis the only dog model available in GI-Sim and GastroPlus. However,physiologies differ between different breeds and this could affectthe quality of the output.5Thisstudy shows that mechanistic PBBM approaches can be used topredict regional differences in absorption as well as the extent ofcolon absorption in dogs with acceptable accuracy. This indicatesthat it is possible to replace in vivo regional absorption studiesin dogs with in silico mechanistic biopharmaceutics modeling usingGI-Sim or GastroPlus in the early assessment of the risk for colonabsorption limitation, which in turn facilitate early decisions toinitiate MR product development or not. Furthermore, the data setused in this study is now available to use for further improvementof the in silico dog colon absorption models.


The transmission of viruses from animal hosts into humans have led to the emergence of several diseases. Usually these cross-species transmissions are blocked by host restriction factors, which are proteins that can block virus replication at a specific step. In the natural virus host, the restriction factor activity is usually suppressed by a viral antagonist protein, but this is not the case for restriction factors from an unnatural host. However, due to ongoing viral evolution, sometimes the viral antagonist can evolve to suppress restriction factors in a new host, enabling cross-species transmission. Here we examine the classical case of this paradigm by reviewing research on APOBEC3 restriction factors and how they can suppress human immunodeficiency virus (HIV) and simian immunodeficiency virus (SIV). APOBEC3 enzymes are single-stranded DNA cytidine deaminases that can induce mutagenesis of proviral DNA by catalyzing the conversion of cytidine to promutagenic uridine on single-stranded viral (−)DNA if they escape the HIV/SIV antagonist protein, Vif. APOBEC3 degradation is induced by Vif through the proteasome pathway. SIV has been transmitted between Old World Monkeys and to hominids. Here we examine the adaptations that enabled such events and the ongoing impact of the APOBEC3-Vif interface on HIV in humans.  Spillover of viruses from animal hosts have led to the emergence of several human diseases including COVID-19, severe acute respiratory syndrome (SARS), Nipah, Ebola, influenza and acquired immunodeficiency syndrome (AIDS) ,3,4. SevThis positive selection of restriction factors leads to differences in functionality, even in similar species, resulting in the ability of these cellular restriction factors to also act as barriers to the cross species transmission of viruses . To succAIDS, which is caused by the human immunodeficiency virus (HIV), is a classic example of spillover of an animal virus into humans. HIV and related immunodeficiency viruses are lentiviruses, a genus of retroviruses where infection is characterized by long incubation periods between infection and onset of disease. The study of the emergence of AIDS also exemplifies how a cellular restriction factor can act as a cross species transmission barrier and how a virus adapts to overcome this barrier. This review examines cross species transmission of the simian immunodeficiency virus (SIV) between primates, with specific focus on the APOBEC3 family of cellular restriction factors that act as single-stranded (ss) DNA cytosine deaminases. The world first became aware of AIDS in the early 1980s, although it had become well established in the human population within the Belgian colonized region of the Congo in the early 1900s . Two lensmm of sooty mangabeys was transmitted to humans at least nine independent occasions giving rise to HIV-2 groups A-I . Aoccurred  115]. . cpz. Navif evolution than A3G, and thus is still important to consider in cross species transmissions .gor Vif interaction site for gorilla A3G also overlaps with HIV-1 Vif interaction site and is at position 16. However, for OWMs, their Vif interaction sites tended to cluster differently on Vif and were at Residue 84 for SMM and Residue 86 for RCM, although this is near Residues 15–17 (Several common interaction interfaces on both Vif and A3 enzymes have been identified. This is evolutionarily favorable since the interaction region can readily evolve without destabilizing the protein . Mutatioes 15–17 . The OWMes 15–17  151]. T. Tgor Vivif can become mutated to produce a protein that does not efficiently induce degradation of A3s [It has been suggested that inefficient interactions of Vif with A3s may actually be beneficial to the virus. During HIV-1 infection, n of A3s . This aln of A3s ,98,154. n of A3s ,155,156.n of A3s . Howevern of A3s ,159. ThuFuture work in the area of A3s and transmission barriers would be interesting to explore with other viruses. Recently A3B has been found to restrict certain herpesviruses and be antagonized by herpesvirus proteins . There mIn summary, A3 enzymes shaped the evolution of SIV and HIV through the ongoing suppression and avoidance related to Vif. As we learn of more viruses that are restricted by A3s in a deamination-dependent manner it would be interesting to realize if there are parallels with the SIV and HIV paradigm or new ones to be discovered. There is still much to learn about A3s and lentiviruses as we discover more A3 polymorphisms, ability of A3s to hetero-oligomerize, and the fate of A3-induced mutations in an organism over the lifetime of an infection.


Social isolation in people living with schizophrenia is associated with poor quality of life and increased symptom severity. Volunteer befriending interventions are a potential strategy for addressing social isolation, but evidence of their effectiveness is limited, particularly in low- and middle-income countries. We assessed the experiences of volunteer befriending and tested its effectiveness for improving the quality of life of patients with schizophrenia in Bosnia and Herzegovina.Between March 2018 and July 2020, we conducted a parallel-group, randomised controlled trial in adults with schizophrenia and poor quality of life at an outpatient clinic in Sarajevo. Patients were randomised to either the intervention, in which they were matched with a volunteer befriender with whom they met fortnightly over the 6-month intervention period, or treatment as usual. The primary outcome was quality of life measured on the Manchester Short Assessment and secondary outcomes were psychiatric symptoms and objective social outcomes. Outcome measurement was conducted by blinded researchers at 6- and 12-months.n = 33) and control arms (n = 32) and 55 (85%) completed follow-up assessments at 6 months. Patients in the intervention showed a significantly more favourable quality of life at 6 months  and 12 months . They also had significantly lower symptom levels at both follow-ups, and a significantly more favourable objective social situation after 12 months. Participants reported largely positive experiences.In total, 65 patients were randomised into the intervention (The exploratory trial conducted at one site found sustained improvements in quality of life and reductions in psychiatric symptoms. This suggests that volunteer befriending may be a feasible and effective treatment for patients with schizophrenia in resource-limited contexts, such as Bosnia and Herzegovina. Pairs were matched based on their interests, preferences and availability, which was discussed during baseline assessments with patients and the recruitment interviews with potential volunteers. The volunteer coordinator then facilitated an initial meeting with each pair.Before the intervention commenced, volunteers attended a one-day training course which covered general information about the programme, the symptoms of schizophrenia, the responsibilities and boundaries of befriending and available resources for supervision and support. They also received regular face-to-face supervision from a senior member of the research team and the volunteer coordinator, and three group supervision sessions were organised for the volunteers to share their experiences. The senior researcher was available to respond to any concerns that arose over the duration of the intervention. After 6 months, volunteers and patients could decide to continue meeting independently.A standardised case report form was used to collect primary and secondary outcome data at baseline, 6 and 12 months. Socio-demographic information and clinical characteristics were assessed at baseline. Health service utilisation were measured at baseline and at each follow-up. Blinded researchers conducted outcome assessment at 6 and 12 months.et al., The primary outcome was subjective quality of life at 6 months, measured using the MANSA, which has been widely used in mental health research .  to compare mean MANSA and BPRS scores in each arm at baseline to 6- and 12-months and proportional odds models (with treatment as a fixed effect) to compare objective social outcomes (SIX). All analysis was conducted on available cases on an intention-to-treat basis. Cohen's Of the 143 patient records screened for participation in the study, 118 met our eligibility criteria and 80 provided written consent to participate. The flow of patients through the study is summarised in n = 33) and control arms (n = 32). Baseline characteristics of both groups are presented in During baseline assessment, five patients were unable to be contacted and 10 were no longer interested. The remaining 65 patients were randomised into the intervention (The mean age of participants was 43 (range: 21–68) and 54% were female. The two groups did not show substantial differences on any of the assessed socio-demographic and clinical characteristics.After advertising the intervention, 110 students applied to participate as volunteers and interviews were scheduled with 75. After checking eligibility, 36 volunteers were chosen to participate, and another six recruited as reserves in the case of drop out. The mean age of participating volunteers was 25 (range 22–29) and 75% were female. All volunteers were either full time (64%) or part-time (36%) university students.Patients in the intervention group were matched with one volunteer to create 33 pairs. Twenty-eight pairs attended an initial meeting facilitated by the study coordinator, while five pairs never met one another. The pairs met on average five times over the 6-month intervention period (range 0–12), and 25 pairs (75%) attended the pre-defined threshold of at least three out of the 12 recommended meetings over 6 months. After the initial meeting, one patient withdrew from the study and one patient was rematched to another volunteer based on patient preferences. Fifteen pairs (45%) continued to meet together after the intervention period had ended.During the 6-month follow-up, four patients in the intervention group and two patients (3) in the control group were seen by a psychologist, and 28 patients (23) in the intervention group and 25 patients (22) in the control group were prescribed anti-psychotics. No patient in any of the two groups received further psycho-social treatments during the study period.Because the pandemic imposed restrictions on social contacts, further face-to-face meetings between volunteers and patients following the 6-month intervention period were difficult or impossible, and 30% of 6-month interviews and all 12-month interviews had to be conducted via telephone or online. There were no other deviations from the protocol.n = 26, control: n = 29) completed follow-up assessments and at 12 months, 50 patients  completed an assessment. The results of outcome assessments are shown in At the end of the 6-month intervention period, 55 patients (intervention: p = 0.003). Similarly, they were more likely to have seen a friend in the past week .After 6 months, there was no difference between participants and controls in the proportion reporting having someone they would call a close friend, or the proportion having seen a friend in the past week. However, at 12-month assessment participants were more likely to have someone they would call a close friend (92%) compared with controls  and at quality of life at 12 months . Significant effects were also observed in psychiatric symptoms at 6 months  and 12 months . For objective social outcomes (SIX), there was a trend towards better scores at 6 months  and significant effect at 12 months .Primary and secondary outcome measurements at all three time points are presented in d = 0.91, p = 0.001).In a sensitivity analysis, we repeated the analysis of the primary outcome of quality of life at 6 months with imputed data for missing patients, using multiple imputation by fully conditional specification. This did not alter the finding of the analysis of available cases as reported before  and training sessions, supervision and support was provided to all volunteers. Valid and reliable outcome measures were used and data collected at multiple time points. These measures including a mix of both self-rated and observer-rated scales, and significant positive effects were found on both self-ratings and observer-ratings.et al., There are also some limitations in this study. The trial was conducted at only one site, which may affect the generalisability. Loss to follow-up at 6 months was 15% and 23% at 12 month, which may introduce some bias. However, this is below average for RCTs of non-pharmacological studies in people with severe mental illness (Szymczynska et al., et al., While there have been few studies examining volunteer befriending in other low- and middle-income countries, a group befriending intervention in Colombia for people with severe mental illness was found to be feasible and acceptable et al.. Get al., et al., et al., et al., et al., Previous studies of befriending interventions for people with severe mental illness in high-income countries have found a positive effect of these programmes on social isolation and social functioning (McCorkle et al., Evidence from other settings has shown that while most people with severe mental illness are willing to participate in befriending interventions, there is considerable variability in their preferences for delivery (Toner et al., et al., et al., et al., et al., Reviews of befriending programmes through volunteers in mental health show a large variation of personal characteristics of volunteers, often reflecting specific aspects of the local context and the set-up of the given programme (Hallett Volunteer befriending is a low-cost intervention, which appears feasible, acceptable and effective for patients with schizophrenia in Bosnia and Herzegovina, a middle-income country. Qualitative data show the intervention was valued by both patients and participants. This suggests volunteer befriending could be delivered and implemented in these settings. Future research is required to better understand implementation processes and explore the characteristics and motivations of volunteers and patients, in order to support the implementation and potential sustainability of these programmes in the longer term. This information may inform the wider adoption and implementation of volunteer befriending programmes in other low-resource settings.


Pan-genome sequence analysis of human population ancestry is critical for expanding and better defining human genome sequence diversity. However, the amount of genetic variation still missing from current human reference sequences is still unknown. Here, we used 486 deep-sequenced Han Chinese genomes to identify 276 Mbp of DNA sequences that, to our knowledge, are absent in the current human reference. We classified these sequences into individual-specific and common sequences, and propose that the common sequence size is uncapped with a growing population. The 46.646 Mbp common sequences obtained from the 486 individuals improved the accuracy of variant calling and mapping rate when added to the reference genome. We also analyzed the genomic positions of these common sequences and found that they came from genomic regions characterized by high mutation rate and low pathogenicity. Our study authenticates the Chinese pan-genome as representative of DNA sequences specific to the Han Chinese population missing from the GRCh38 reference genome and establishes the newly defined common sequences as candidates to supplement the current human reference. Qiuhui Li et al. report a pan-genome sequence study in Han Chinese genomes. They find 276 Mbp of DNA sequences (absent in the current human reference), and report that 46.646 Mbp common sequences improved the accuracy of variant calling and mapping rate when added to the reference genome. The current reference assembly for humans, GRCh38, is being improved continually by researchers around the globe, but it still under-represents the human population. Currently, it is constructed mostly from individual sequences of Caucasian and African ancestries3, which effectively limits genomic analysis of other distinct population ancestries. As a consequence, some population-specific genomic variants cannot be detected when aligned to the current reference. This lack of vital information on genetic diversity makes it almost impossible to uncover some genetic links with disease.In genome-based studies, the creation of highly enriched reference sequences is fundamental for dissecting the associations of genetic variants with human diseases. To capture these variations as much as possible, the genomics field has been trying to build and perfect human reference genomes. In the majority of studies, for different species, the approach is still to rely on individual sequence assessments as a reference for each species. However, the use of a single reference genome often misses important variations or fails to detect sequence differences6. Others have attempted to assemble reference genomes across diverse populations, such as the Simons Genome Diversity Project7 and the TOPMed Project8. Still other projects have used the genomes of homogenous populations to discover novel sequence insertions. The Genome of the Netherlands Project released 7718 novel segments9, while 3791 novel non-repetitive sequences were called from 15,219 Icelanders10. While the identification of sequence deletions or insertions may be beneficial for exploring evolutionary directions7, for understanding the relevance of abnormally expressed genes and novel sequences11 and for discovering diseases specific to a particular population ancestries10, these data have not been integrated into a new, more representative sequence reference for the human genome.To increase diversity of the current reference genomes in human, several personal genomes have been assembled from subpopulations, including Korean, Caucasian, and Mongolian genomes12. Pan-genome is a collection of all DNA sequences that occur in a species. Ideally, pan-genomic analysis better captures unexplored or missed variants to improve the decoding of the genetic basis of human diseases1. Advances in sequencing and assembly technologies, as well as the decline in sequencing costs, facilitate pan-genome studies across a large range of species, from bacteria to plants to humans18. Considering the computational complexity of assembling deep sequenced human genomes de novo and combining them, a feasible strategy for improved references is to organize population-specific pan-genomes and to extend genetic diversity of the existing pan-genomes. In this regard, two human pan-genomes have been published. The first whole-genome sequencing and assembly project for human pan-genome was reported on in 2010. This project identified approximately 5 Mbp of novel sequences absent in the reference genome for each of the assemblies and predicted that the total size of the novel sequences reached 19–40 Mbp19. Later, a project to create an African pan-genome (APG) derived from 910 African genomes detected 296 Mbp of novel DNA sequences, considerably more than the 2010 project20. A more recent report named HUPAN, a human pan-genome construction and analysis system that had built the first pan-genome of Chinese, identified 29.5 Mbp of Chinese-specific novel sequences derived from 275 individuals21. These studies show that the sizes of the pan-genomes in humans are variable and therefore ill-defined and the studies do not indicate whether pan-genomes grow indefinitely as more individual inputs are sequenced. In other words, it remains unclear whether the size of pan-genomes will reach a maximum or continue to grow with increasing number of individuals. More importantly, biological interpretation of the predicted sequences needs to be implemented for defining the pan-genomes.In order to acquire a more complete picture of genetic variations, “pan-genome” coverage has been proposed of both the core and variably distributed genomes of a speciesIn this study, we used deep sequencing data of 486 Han Chinese individuals to build a Chinese pan-genome (CPG), consisting of 276 Mbp DNA sequences that are absent from the current human reference. The novel sequences could be classified into two components: individual-specific sequences with an average of 0.472 Mbp and common sequences  totaling 46.646 Mbp. Based on this two-component model, we showed that the total size of common sequences could increase linearly with the number of individuals, hence constructing pan-genomes for huge populations would be impractical. Nevertheless, we also showed that by defining “common sequences” as sequences whose occurrence within a population is above a fixed percentage, their total size would remain constant regardless of individuals. Finally, for applications, the distinct characteristics of the CPG common sequences improved variant calling and sequence alignments, and could be used to identify special regions with high mutation rates and low pathogenicity. Thus, the common sequences defined in CPG could be used as new “decoys” and effectively increase the diversity of current human reference genome.27. Other than ethnicity as “Han Chinese”, all individuals were de-identified and personal details were unavailable. We used verifyBamID28 to detect sample contamination and removed any individuals with an “estimate of contamination” greater than 0.03. We then analyzed the remaining 1000 genome project individuals using Principal Component Analysis and excluded any that significantly deviated from the CHB (Han Chinese in Beijing) group. In total, 486 individuals were employed in this study, with an average sequencing depth of 53.6-fold .We selected whole-genome sequencing datasets from a pool of short-read sequencing datasets of normal Han Chinese individuals from the Novo-Zhonghua Genome Database. It is a commercial reference database of Chinese by Novogene Co., Ltd., Beijing, China, and has been used for multiple studies29 to analyze the population components of the 486 individuals  well and were representative of the diversities of the population.We used ADMIXTURE (version 1.3.0)als Fig. . At k = 30 , we have established an analysis system that carried out four major tasks Fig. . Firstly31. The result showed only 0.3% of CPG aligned to nonhuman sequences  of mutations at each genomic position. We found that the mean pathogenicity scores of the insertion regions of the common and individual-specific sequences were 4.295 and 5.126, respectively . CADDely Fig. . A highe34. The novel sequences were also enriched with the regulatory regions, such as CTCF binding sites and promoters To seek the origin of the sequences, we aligned CPG to the chimpanzee genome (GCF_002880755.1) by BWA (version 0.7.12). We considered a novel sequence to be of chimpanzee origin if it aligned to the chimpanzee genome with over 90% identity and 80% coverage. 0.96% of the novel sequences passed the thresholds, suggesting that these sequences are ancestral to humans. Of the individual-specific sequences, 0.47% (compared to 7.05% of the common sequences) was aligned to chimpanzee.36. Based on a minimum requirement of 90% identity, the percentages of alignments to Chinese (23.7%) and Korean (24.5%) were much higher than to African (16.3%) 21. We aligned CPG to APG and HCPG using thresholds of 90% identity and 50% coverage and vice versa. CPG aligned to both APG and HCPG with a low mapping rate of less than 10% (4.89% (13.51 Mbp in size) and 5.04% (13.93 Mbp in size)). When aligning the common sequences in CPG to HCPG, the mapping rate increased to 14.5%. Relatively, alignment percentage of HCPG to CPG was 8.4%, higher than 1.8% of the APG to CPG , KOREF1.0 (Korean), and NA18507 (African)3%) Fig. . This prCPG Fig. , c. This−10 e-value ; 2.7% alphoid satellite DNA ). However, it’s not the case in CPG. dna-brnn shows that only 0.5%  and 0.7%  of the bases in common and individual sequences, respectively, were centromeric , indicating that the improvements were indeed from adding the common sequences instead of insufficient sequencing depth.An important application of novel sequences is to improve the quality of variant calling. We combined the common sequences with the full GRCh38  as a new reference  analysis pipeline21, the size of novel sequences in 50 individuals was 22 Mbp and was further increased only by 4 Mbp and 3 Mbp, respectively with the individual numbers expanded to 100 and 150. Although HUPAN’s novel sequences were not explicitly classified into individual-specific and common, the slowdown in increase might suggest that the total size of common sequences was converging and eventually bounded. Similar slowdown patterns were also observed in the common sequences of CPG, but it was difficult to draw a definite conclusion since the sample size (486) was too small compared to the whole Chinese population. We noticed that the occurrence threshold was an important factor for estimating the total size of common sequences and proposed to use a proportionate occurrence frequency, OF, when defining common sequences. Our analysis showed that the total size of common sequences was bounded if OF was being used and became unbounded if a constant occurrence threshold was used instead, which was not in agreement with our previous opinion21. However, it is worth mentioning that the common sequence sizes estimated from CPG and APG are not similar under the same OF. The main reason is possibly related to composition of populations, sequencing technologies, and analysis methods, etc. In APG, more individuals are needed to find the common sequences in the African population due to a higher heterogeneity. The slower growth observed in HUPAN might be due to the genome assembler used in their study. HUPAN has used 185 deep sequencing and 90 assembled Han Chinese genomes. While the 185 deep sequencing genomes were PCR free and assembled using SGA46, the 90 assembled Han Chinese were sequenced much earlier in 2013 with a PCR-based protocol and assembled using SOAPdenovo238. For PCR-based sequencing data—the DNA sequences that contain either excessively high or excessively low GC may be partially missed from the result. SOAPdenovo247 was developed by the corresponding author of this study. SOAPdenovo2 is an assembler that maximizes sequence contiguity in trade of sequence completeness, especially for those repetitive sequences that are more commonly observed in novel sequences. In the present study, we used only PCR free genome sequences and assembler MEGAHIT, which was designed to maximize sequence completeness. We expected the use of PCR free data and MEGAHIT will largely maximize the sequence completeness of this study, and we indeed observed a larger size of novel sequences. But we also realized that not all these additional novel sequences are correct, and they need to be further validated through long-read assemblies or even wet-lab experiments in the future.One challenge in human pan-genome research is to determine whether there exists an upper bound limit to the size of a pan-genome. This leads to a further question of how many individuals are needed for finding population-specific common sequences. Previous analyses reported that some species like human pathogens and bacteria have an open pan-genome, presuming an infinite model of the pan-genomes in these species7. However, this project did not provide the detailed description for the result. Our common sequences have significantly elevated the alignment rate in both our datasets and public human genome information. This finding supports the possibility of the common sequences of CPG as representative of Han Chinese genomes missing from the current reference. Furthermore, a previous study used multiple reference genomes to replace a single reference genome for improving variation calling on human genome48. However, that study was inconclusive due to testing only low-depth (10×) sequencing reads, which are prone to excessive amounts of false-positive and false-negative variants. We used 60-fold and 300-fold depth sequencing reads from three populations to call variants. The improvements were consistently observed in all three populations and when increasing from normal-depth to ultra-high depth.In general, current human genome analysis starts by aligning unknown sequences to the current references and then discarding the unmapped data. This traditional method fails to detect some disease-related mutations and other important genetic variant sites that are harbored on the unaligned sequences. Previously, Simons Genome Diversity Project identified 5.8 Mbp of novel sequences and reported that these sequences can improve read mapping49. Under natural selection, benign mutations are tolerated and inherited by the next generations. Thus these benign-likely sequences became common ones. The characteristics of the CPG common sequences may be explained by their relationships with population-specific evolution. Evolution is the process by which a species must overcome new conditions and create a new set of unique metabolic reactions to a particular environmental stress50. As humans dispersed out of Africa, the subpopulations have experienced selective pressures affecting genome integrity. Under this situation, a high mutation rate is favored to produce novel genotypes at loci which are responsible for adaptation to new environments51. The adaptive response could accelerate the evolutionary rate of the lineage and the functional evolution of specific stress-response proteins53. We presume that the high mutation rate of the CPG common sequences may reflect the stress‐directed adaptive variations due to phenotype evolution that results from increases in mutation rates of genes responding to a particular stress54. Since there are three key divergences of modern humans, between Europeans and Asians, between Africans and non-Africans, and between the Khoe–San of southern Africa and other modern humans55, the insertion regions of the common sequences could be the hotspots that promote evolution not only for Chinese but also for Eastern Asians.The insertion points of the common sequences held the highest mutation rates and the lowest pathogenicity scores, which were even lower than the average pathogenicity of GRCh38. A low pathogenicity indicates that the mutation at an insertion point is relatively benign. Mutation is a raw material of evolution and the change in DNA sequences are likely beneficial to its organism19 (estimated a complete human pan-genome contains ~19–40 Mbp of novels sequence missing from the reference genome NCBI Build 36.3), and provided a new perspective of estimating pan-genome size. However, its accuracy remains to be verified by more whole-genome sequenced Chinese individuals in the future. The distinct characteristics and application efficiency of the CPG common sequences offer a high possibility of the 46.646 Mbp sequences as new “decoys” and are likely supplementary to the current human reference genome. We suggested a possible connection of the common sequences with population-specific evolution, yet the biological significance of most CPG sequences remains unknown and needs to be interpreted. With an accumulating amount of deep sequenced data becoming available, current pan-genomic studies will be extended to be more comprehensive population-scale and to explore the biological meaning of the detected genetic variations.In summary, we first constructed a two-component pan-genome that could represent the Han Chinese genome sequences missed from the current reference. Our growth analysis suggested that the size of common sequences is not bounded by an upper limit with an increasing population when using a constant occurrence threshold. Instead, we introduced the concept of occurrence frequency (OF), which suggested the possibility of constructing bounded-size pan-genomes by dynamically adjusting the threshold based on the sampled population. We realized the limitation of our conclusion using only 486 individuals. With more individuals studied, more individual-specific sequences defined in this study would turn out to be common sequences. But meanwhile, more individual-specific sequences will also be found. Our current study overthrew the pan-genome size estimation we made in 201056. Read pairs with at least one unaligned read were classified as unaligned pairs  or semi-aligned pairs . All unaligned reads were assembled into contigs using MEGAHIT (version 1.1.1)57.We used BWA (version 0.7.12) to align the raw reads  of the 486 individuals to GRCh38.p13 and obtained all unaligned reads by SAMtools (version 0.1.19)To confirm the uniqueness of the assembled contigs, we aligned the contigs to GRCh38.p13 containing patches and alternative locations. All contigs mapped with ≥90% identity and ≥80% coverage were discarded. We aligned the remaining contigs to non-cordate genomes by BLASTN (version 2.9.0) and removed the sequences aligned with >90% identity and >95% coverage to ensure that no contaminant sequences remained in the novel contigs .All unaligned reads in semi-aligned pairs were mapped to the remaining contigs by Bowtie259 (with options–minmatch 15–breaklen 1). If at least one alignment started within 5 bp of the placed end and all alignments of the contig were consistent with one another, the exact position of the placed was then derived from the alignment result.The next step was to determine the exact position of the placed end in GRCh38. We considered the aforementioned 2000-bp genomic region in which the linked mates were mapped (500 bp would be attached to the start and end of this genomic region respectively). We aligned the terminal 100 bp of the placed end to the region using NUCmer (version 3.2.3)We classified contigs based on their placement status. If both ends of a contig were placed on the same chromosome with the same orientation, we called the contig both-end-placed (BEP). If only one end of the contig was placed, we called it single-end-placed (SEP). We further divided SEP contigs into left-end-placed (LEP) and right-end-placed (REP) contigs depending on which end of the contig was placed when aligned to the positive strand of GRCh38. If neither end of the contig was placed, we called it an unplaced contig.We defined the placement location of an LEP as the leftmost base-pair of the leftmost alignment and the placement location of an REP as the rightmost base-pair of the rightmost alignment. The insertion position of an LEP was defined as the rightmost base-pair of the leftmost alignment and the insertion position of an REP as the leftmost base-pair of the rightmost alignment. The placement location and insertion position of each end of a BEP contig are defined similarly as an LEP or REP. Note that the two insertion points of a BEP contig may coincide.Based on placement positions and sequence similarity, we grouped the placed contigs of 486 individuals into clusters as follows:60 to cluster them together and selected the longest one as the representative of the cluster. Next, we aligned all contigs in the cluster to the representative by NUCmer (using default parameters). Contigs that could not be aligned to its representative were removed from the cluster. To make the clusters more complete, SEP contigs and unplaced contigs were aligned to contigs in BEP clusters by BLASTN. These contigs were added to the BEP cluster if they were fully contained with >99% identity, and covered >80% of the aligned BEP. If the coverage of the BEP contig was under 80%, the SEP or unplaced contig was added to the BEP cluster only if it met two conditions: (1) the SEP or the unplaced contig had at least 5 mapped linked mates and (2) at least 25% of its mapped linked mates were within 2000 bp of the BEP cluster’s placement location.For BEP contigs placed within 20 bp of each other, we used BEDtools (version 2.25.0)We processed LEP contigs and REP contigs separately. As in the case for BEP contigs, we clustered LEP contigs placed within 20 bp of each other and selected the longest one as the cluster representative. LEP clusters within 20 bp of the placement location of any BEP cluster were discarded. For each discarded LEP cluster, its contigs were aligned to the corresponding BEP cluster’s representative using BLASTN. Contigs aligned with >90% identity and >80% coverage were added to the BEP cluster. Next, we removed from LEP clusters any contig which could not be aligned to its representative using NUCmer. To make the clusters more complete, unplaced contigs (which were not added to BEP clusters) were aligned to representatives of LEP clusters using BLASTN. An unplaced contig aligned with 100% coverage, 99% identity and covering 80% of the representative would be added to the LEP cluster. If the coverage of the representative was under 80%, the unplaced contig was added to the LEP cluster only if it met two conditions: 1) the unplaced contig had at least 4 mapped linked mates and 2) at least 25% of the mapped linked mates were within 2000 bp of the LEP cluster’s placement location. These steps were repeated for REP contigs to obtain REP clusters.PopIns61 .31, Trf (version 4.0.9)62, and RepeatMasker (version 4.0.7)63. For unplaced contigs with a continuous repeat percentage greater than 50%, we removed the masked portions and retained the leftover sequences longer than 200 bp. Lastly, we utilized cd-hit-est version 4.8.1 (with options “-c 0.9 -n 8”) to cluster the resulting (non-repetitive) unplaced contigs and obtain their representatives.For the remaining unplaced contigs which were not added to clusters in the above steps, we only kept their non-repetitive portions, since highly repetitive sequences without insertion points were likely due to contamination. We built a workflow to mask the repetitive portions using Dustmaskerp by sampling p individuals out of 486. We then identified all individual-specific sequences and common sequences among these p individuals and calculated their total sizes, denoted by Ip and Cp, respectively. Note that common sequences are defined as those appearing as least twice in the p individuals, thus a common sequence for 486 individuals may not be a common sequence for p individuals. The values of Ip and Cp were averaged over 1000 repetitions of sampling. We performed the experiment for p from 1 up to 486 and the results are depicted in Fig. We performed an experiment to study the growth of the Chinese pan-genome size with respect to population size. In the experiment, we estimated the pan-genome size for population size To study how common sequences were affected by the occurrence threshold, the above experiment was repeated for different occurrence thresholds from 5 up to 200. The experiment results for CPG and APG are shown in Figs. https://krishna.gs.washington.edu/download/CADD/v1.6/GRCh38/whole_genome_SNVs.tsv.gz) to compute the pathogenicity score of each insertion point. For any genome position, CADD provides the score of three candidate SNVs . The pathogenicity score of the insertion point of a SEP contig was calculated as the average of the six scores of the adjacent 2 bp. The pathogenicity score of a BEP contig was calculated as an average of the scores of all the base pairs between the two insertion points. The pathogenicity score of each contig is listed in Supplementary Data We used the C-scores of Single Nucleotide Variants (SNV) provided by CADD . The statistical assumptions of the growth model of common sequence are detailed in Supplementary Note All of our statistical analyses were carried out on biologically independent samples (Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Supplementary FilesSupplementary Data 1-9Reporting Summary