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Background Orphan drug development faces numerous challenges, including low disease prevalence, patient population heterogeneity, and strong presence of paediatric patient populations. Consequently, clinical trials for orphan drugs are often smaller than those of non-orphan drugs, and they require the development of efficient trial designs relevant to small populations to gain the most information from the available data. The International Rare Diseases Research Consortium (IRDiRC) is aimed at promoting international collaboration and advance rare diseases research worldwide, and has as one of its goals to contribute to 1000 new therapies for rare diseases. IRDiRC set up a Small Population Clinical Trials (SPCT) Task Force in order to address the shortcomings of our understanding in carrying out clinical trials in rare diseases. Results The IRDiRC SPCT Task Force met in March 2016 to discuss challenges faced in the design of small studies for rare diseases and present their recommendations, structured around six topics: different study methods/designs and their relation to different characteristics of medical conditions, adequate safety data, multi-arm trial designs, decision analytic approaches and rational approaches to adjusting levels of evidence, extrapolation, and patients’ engagement in study design. Conclusions Recommendations have been issued based on discussions of the Small Population Clinical Trials Task Force that aim to contribute towards successful therapy development and clinical use. While randomised clinical trials are still considered the gold standard, it is recommended to systematically take into consideration alternative trial design options when studying treatments for a rare disease. Combining different sources of safety data is important to give a fuller picture of a therapy’s safety profile. Multi-arm trials should be considered an opportunity for rare diseases therapy development, and funders are encouraged to support such trial design via international networks. Patient engagement is critical in trial design and therapy development, a process which sponsors are encouraged to incorporate when conducting trials and clinical studies. Input from multiple regulatory agencies is recommended early and throughout clinical development. Regulators are often supportive of new clinical trial designs, provided they are well thought through and justified, and they also welcome discussions and questions on this topic. Parallel advice for multiregional development programs should also be considered.

The coastal Cactus Wren (Campylorhynchus brunneicapillus) persists in small and fragmented populations throughout southern California that are subject to genetic drift and inbreeding. We combined individual banding and resighting data and genotyped individuals at 22 microsatellite loci to assess whether heterozygosity was associated with survival across three regional Cactus Wren populations on conserved lands in Orange and San Diego Counties between 2009 and 2020. Using Cormack‐Jolly‐Seber models (CJS) to analyze the 5‐year capture histories of 528 individual wrens, we found that age class (hatch year or after hatch year) was the strongest predictor of survival. Individual heterozygosity and precipitation also had positive effects on survival, with survival up to 2 times higher in the most heterozygous individuals compared to the least and up to 1.5 times higher in high precipitation years versus drought years. Multi‐locus heterozygosity was significantly correlated across loci, suggesting that inbreeding depression is likely driving the association between survival and heterozygosity. Study results support that genetic rescue efforts that reduce inbreeding have the potential to improve fitness and mitigate further loss of genetic variation in managed populations. We conducted individual banding and resighting studies and collected genetic data in populations of coastal Cactus Wrens in Orange and San Diego Counties between 2009 and 2020. We found that survival was much lower for hatch year birds than after hatch year birds and that higher precipitation and heterozygosity had positive effects on survival. Results support that genetic rescue efforts have the potential to improve fitness in managed populations in this species of conservation concern.

Background Most statistical design and analysis methods for clinical trials have been developed and evaluated where at least several hundreds of patients could be recruited. These methods may not be suitable to evaluate therapies if the sample size is unavoidably small, which is usually termed by small populations. The specific sample size cut off, where the standard methods fail, needs to be investigated. In this paper, the authors present their view on new developments for design and analysis of clinical trials in small population groups, where conventional statistical methods may be inappropriate, e.g., because of lack of power or poor adherence to asymptotic approximations due to sample size restrictions. Method Following the EMA/CHMP guideline on clinical trials in small populations, we consider directions for new developments in the area of statistical methodology for design and analysis of small population clinical trials. We relate the findings to the research activities of three projects, Asterix, IDeAl, and InSPiRe, which have received funding since 2013 within the FP7-HEALTH-2013-INNOVATION-1 framework of the EU. As not all aspects of the wide research area of small population clinical trials can be addressed, we focus on areas where we feel advances are needed and feasible. Results The general framework of the EMA/CHMP guideline on small population clinical trials stimulates a number of research areas. These serve as the basis for the three projects, Asterix, IDeAl, and InSPiRe, which use various approaches to develop new statistical methodology for design and analysis of small population clinical trials. Small population clinical trials refer to trials with a limited number of patients. Small populations may result form rare diseases or specific subtypes of more common diseases. New statistical methodology needs to be tailored to these specific situations. Conclusion The main results from the three projects will constitute a useful toolbox for improved design and analysis of small population clinical trials. They address various challenges presented by the EMA/CHMP guideline as well as recent discussions about extrapolation. There is a need for involvement of the patients’ perspective in the planning and conduct of small population clinical trials for a successful therapy evaluation.

This paper reviews strategies and methods to improve accuracies of genomic predictions from the perspective of a numerically small population. Improvements are realized by influencing one or both of the main factors: (1) improve or increase genomic connections to phenotypic records in training data. (2) Models and strategies to focus genomic predictions on markers closer to the causative variants. Combining populations into a joint reference population results in high improvements when combining populations of the same breed and diminishes as the genetic distance between populations increases. For distantly related breeds sophisticated Bayesian variable selection models in combination with denser markers sets or functional subsets of markers is needed. This is expected to be further improved by the efficient use of sequence information. In addition predictions can be improved by the use of phenotypes of genotyped and non-genotyped cows directly. For a small population the optimal approach will combine the above components.

Increasing habitat fragmentation leads to wild populations becoming small, isolated, and threatened by inbreeding depression. However, small populations may be able to purge recessive deleterious alleles as they become expressed in homozygotes, thus reducing inbreeding depression and increasing population viability. We used whole-genome sequences from 57 tigers to estimate individual inbreeding and mutation load in a small–isolated and two large–connected populations in India. As expected, the small–isolated population had substantially higher average genomic inbreeding (F ROH = 0.57) than the large–connected (F ROH = 0.35 and F ROH = 0.46) populations. The small–isolated population had the lowest loss-of-function mutation load, likely due to purging of highly deleterious recessive mutations. The large populations had lower missense mutation loads than the small–isolated population, but were not identical, possibly due to different demographic histories. While the number of the loss-of-function alleles in the small–isolated population was lower, these alleles were at higher frequencies and homozygosity than in the large populations. Together, our data and analyses provide evidence of 1) high mutation load, 2) purging, and 3) the highest predicted inbreeding depression, despite purging, in the small–isolated population. Frequency distributions of damaging and neutral alleles uncover genomic evidence that purifying selection has removed part of the mutation load across Indian tiger populations. These results provide genomic evidence for purifying selection in both small and large populations, but also suggest that the remaining deleterious alleles may have inbreeding-associated fitness costs. We suggest that genetic rescue from sources selected based on genome-wide differentiation could offset any possible impacts of inbreeding depression.

Deleterious mutations continuously accumulate in populations, building up a burden that can threaten their survival, particularly in small populations when inbreeding exposes recessive deleterious effects. Notwithstanding, this process also triggers genetic purging, which can reduce the deleterious burden and mitigate fitness inbreeding depression. Here, we analyzed 20 whole genomes from the endangered Iberian lynx and 28 from the widespread Eurasian lynx, sister species which constitute a good model to study the dynamics of deleterious mutation burden under contrasting demographies, manifested in the consistently smaller population size and distribution area of the Iberian lynx. We also derived analytical predictions for the evolution of the deleterious burden following a bottleneck. We found 11% fewer derived alleles for the more putatively deleterious missense category in the Iberian lynx than in the Eurasian lynx, which, in light of our theoretical predictions, should be ascribed to historical purging. No signs of purging were found in centromeres nor in the X chromosome, where selection against recessive deleterious alleles is less affected by demography. The similar deleterious burden levels for conspecific populations despite their contrasting recent demographies also point to sustained differences in historical population sizes since species divergence as the main driver of the augmented purging in the Iberian lynx. Beyond adding to the ongoing debate on the relationship between deleterious burden and population size, and on the impact of genetic factors in endangered species viability, this work contributes a whole-genome catalog of deleterious variants, which may become a valuable resource for future conservation efforts.

Allee effects driven by predation can play a strong role in the decline of small populations but are conventionally thought to occur when generalist predators target specific prey (i.e. type II functional response). However, aside from direct consumption, fear of predators could also increase vigilance and reduce time spent foraging as population size decreases, as has been observed in wild mammals living in social groups. To investigate the role of fear on fitness in relation to population density in a species with limited sociality, we exposed varying densities of Drosophila melanogaster to mantid predators either during an experimental breeding season or non-breeding season. The presence of mantids in either season decreased the reproductive performance of individuals but only at low breeding densities, providing evidence for an Allee effect. We then used our experimental results to parametrize a mathematical model to examine the population consequences of fear at low densities. Fear tended to destabilize population dynamics and increase the risk of extinction up to sevenfold. Our study provides unique experimental evidence that the indirect effects of the presence of predators can cause an Allee effect and has important consequences for our understanding of the dynamics of small populations.

Background Random-effects meta-analysis is commonly performed by first deriving an estimate of the between-study variation, the heterogeneity , and subsequently using this as the basis for combining results, i.e., for estimating the effect , the figure of primary interest. The heterogeneity variance estimate however is commonly associated with substantial uncertainty, especially in contexts where there are only few studies available, such as in small populations and rare diseases. Methods Confidence intervals and tests for the effect may be constructed via a simple normal approximation, or via a Student- t distribution, using the Hartung-Knapp-Sidik-Jonkman (HKSJ) approach, which additionally uses a refined estimator of variance of the effect estimator. The modified Knapp-Hartung method (mKH) applies an ad hoc correction and has been proposed to prevent counterintuitive effects and to yield more conservative inference. We performed a simulation study to investigate the behaviour of the standard HKSJ and modified mKH procedures in a range of circumstances, with a focus on the common case of meta-analysis based on only a few studies. Results The standard HKSJ procedure works well when the treatment effect estimates to be combined are of comparable precision, but nominal error levels are exceeded when standard errors vary considerably between studies (e.g. due to variations in study size). Application of the modification on the other hand yields more conservative results with error rates closer to the nominal level. Differences are most pronounced in the common case of few studies of varying size or precision. Conclusions Use of the modified mKH procedure is recommended, especially when only a few studies contribute to the meta-analysis and the involved studies’ precisions (standard errors) vary.

There is no general explanation for why species have restricted geographic distributions. One hypothesis posits that range expansion or increasing scarcity of suitable habitat results in accumulation of mutational load due to enhanced genetic drift, which constrains population performance toward range limits and further expansion. We tested this hypothesis in the North American plant, Arabidopsis lyrata. We experimentally assessed mutational load by crossing plants of 20 populations from across the entire species range and by raising the offspring of within-and between-population crosses at five common garden sites within and beyond the range. Offspring performance was tracked over three growing seasons. The heterosis effect, depicting expressed mutational load, was increased in populations with heightened genomic estimates of load, longer expansion distance or long-term isolation, and a selfing mating system. The decline in performance of within-population crosses amounted to 80 %. Mutation accumulation due to past range expansion and long-term isolation of populations in the area of range margins is therefore a strong determinant of population-mean performance, and the magnitude of effect may be sufficient to cause range limits.

Isolation of small populations can reduce fitness through inbreeding depression and impede population growth. Outcrossing with only a few unrelated individuals can increase demographic and genetic viability substantially, but few studies have documented such genetic rescue in natural mammal populations. We investigate the effects of immigration in a subpopulation of the endangered Scandinavian arctic fox (Vulpes lagopus), founded by six individuals and isolated for 9 years at an extremely small population size. Based on a long-term pedigree (105 litters, 543 individuals) combined with individual fitness traits, we found evidence for genetic rescue. Natural immigration and gene flow of three outbred males in 2010 resulted in a reduction in population average inbreeding coefficient (f), from 0.14 to 0.08 within 5 years. Genetic rescue was further supported by 1.9 times higher juvenile survival and 1.3 times higher breeding success in immigrant first-generation offspring compared with inbred offspring. Five years after immigration, the population had more than doubled in size and allelic richness increased by 41%. This is one of few studies that has documented genetic rescue in a natural mammal population suffering from inbreeding depression and contributes to a growing body of data demonstrating the vital connection between genetics and individual fitness.