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The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs—for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly. This Review provides a perspective on the development of non-cancer therapies based on human genetics studies and suggests measures that can be taken to streamline the pipeline from initial genetic discovery to approved therapy.

The global outbreak of a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) highlighted a requirement for two pronged clinical interventions such as development of effective vaccines and acute therapeutic options for medium-to-severe stages of \"coronavirus disease 2019\" (COVID-19). Effective vaccines, if successfully developed, have been emphasized to become the most effective strategy in the global fight against the COVID-19 pandemic. Basic research advances in biotechnology and genetic engineering have already provided excellent progress and groundbreaking new discoveries in the field of the coronavirus biology and its epidemiology. In particular, for the vaccine development the advances in characterization of a capsid structure and identification of its antigens that can become targets for new vaccines. The development of the experimental vaccines requires a plethora of molecular techniques as well as strict compliance with safety procedures. The research and clinical data integrity, cross-validation of the results, and appropriated studies from the perspective of efficacy and potently side effects have recently become a hotly discussed topic. In this review, we present an update on latest advances and progress in an ongoing race to develop 52 different vaccines against SARS-CoV-2. Our analysis is focused on registered clinical trials (current as of November 04, 2020) that fulfill the international safety and efficacy criteria in the vaccine development. The requirements as well as benefits and risks of diverse types of SARS-CoV-2 vaccines are discussed including those containing whole-virus and live-attenuated vaccines, subunit vaccines, mRNA vaccines, DNA vaccines, live vector vaccines, and also plant-based vaccine formulation containing coronavirus-like particle (VLP). The challenges associated with the vaccine development as well as its distribution, safety and long-term effectiveness have also been highlighted and discussed.

Sickle cell disease (SCD) and cystic fibrosis (CF) are severe autosomal recessive disorders associated with intermittent disease exacerbations that require hospitalizations, progressive chronic organ injury, and substantial premature mortality. Research funding is a limited resource and may contribute to health care disparities, especially for rare diseases that disproportionally affect economically disadvantaged groups. To compare disease-specific funding between SCD and CF and the association between funding and research productivity. This cross-sectional study examined federal and foundation funding, publications indexed in PubMed, clinical trials registered in ClinicalTrials.gov, and new drug approvals from January 1, 2008, to December 31, 2018, in an estimated US population of approximately 90 000 individuals with SCD and approximately 30 000 individuals with CF. Federal and foundation funding, publications indexed in PubMed, clinical trial registrations, and new drug approvals. From 2008 through 2018, federal funding was greater per person with CF compared with SCD (mean [SD], $2807 [$175] vs $812 [$147]; P < .001). Foundation expenditures were greater for CF than for SCD (mean [SD], $7690 [$3974] vs $102 [$13.7]; P < .001). Significantly more research articles (mean [SD], 1594 [225] vs 926 [157]; P < .001) and US Food and Drug Administration drug approvals (4 vs 1) were found for CF compared with SCD, but the total number of clinical trials was similar (mean [SD], 27.3 [6.9] vs 23.8 [6.3]; P = .22). The findings show that disparities in funding between SCD and CF may be associated with decreased research productivity and novel drug development for SCD. Increased federal and foundation funding is needed for SCD and other diseases that disproportionately affect economically disadvantaged groups to address health care disparities.

Anticancer agents with favorable safety profiles, such as immunomodulators and molecular‐targeted agents, are increasingly used in recent cancer therapy, and clinical trials for these agents increasingly incorporate healthy volunteers as well as cancer patients. Clinical trials in healthy volunteers allow intensive safety monitoring and intensive pharmacokinetic sampling, enabling the acquisition of high‐quality, broad‐range pharmacokinetic and safety data within a shorter timeframe and at lower cost. Leveraging these advantages by utilizing healthy volunteer trials in place of patient trials may contribute to more rapid and efficient implementation of anticancer drug development and is expected to help eliminate the “drug lag” and “drug loss” that have been concerns in Japan's research and development process in recent years. In this study, we investigated the clinical data packages of newly approved anticancer agents in Japan from April 2014 to March 2024 and evaluated trends in cases in which data from Japanese healthy volunteers were obtained. We then summarized key considerations for anticancer drug development in Japan regarding whether data from Japanese healthy volunteers should be obtained. Study Highlights What is the current knowledge on the topic? ○HVs are increasingly utilized in anticancer drug development worldwide. However, the use of Japanese HVs in oncology drug development remains limited, and no specific criteria for including HVs are defined in the current Japanese guidelines. What question did this study address? ○This study addressed the conditions under which Japanese HV data have been obtained by reviewing the clinical data packages of anticancer agents approved in Japan between April 2014 and March 2024. What does this study add to our knowledge? ○This study identifies decision criteria for the effective use of Japanese HV data in oncology drug development in Japan. These criteria support an attractive approach to engaging Japanese HVs early in the clinical development of anticancer drugs, which can substantially shorten the time needed to obtain high‐quality PK and safety data at lower cost before MRCTs. How might this change clinical pharmacology or translational science? ○The conclusions from this study support decision making on whether to include Japanese HVs in oncology drug development in Japan. Effective utilization of Japanese HV data may accelerate and improve the efficiency of anticancer drug development in Japan, potentially helping to reduce “drug lag” and “drug loss”.

This paper begins to build a theoretical framework that would enable the pharmaceutical industry to use network complexity measures as a way to identify drug targets. The variability of a betweenness measure for a network node is examined through different methods of network perturbation. Our results indicate a robustness of betweenness centrality in the identification of target genes.

Despite advances in novel therapeutic modalities, such as molecularly targeted agents and immunotherapies, the probability of success in Phase III oncology trials remains low. This study quantitatively characterized success probabilities of Phase III trials in oncology drug development and evaluated the factors associated with trial success. Phase III interventional oncology drug trials registered at ClinicalTrials.gov between 2007 and 2023 with publicly available primary endpoint results were included. Trial success was defined as the achievement of at least one primary endpoint, and tumor‐specific success probabilities were calculated. Multivariable logistic regression analyses were conducted to assess the association between trial success and key trial characteristics. We analyzed 824 trials (358 successful and 466 unsuccessful). Overall success probabilities were comparable between solid tumors and hematologic malignancies, although substantial heterogeneity was observed across solid tumors, with particularly low success probabilities for central nervous system tumors and pancreatic cancer. Multivariable analyses showed that biomarker‐based patient selection, more recently initiated trials, line of therapy, and the number of primary endpoints were associated with trial success. Trials evaluating molecularly targeted therapies and those with short‐term evaluable endpoints showed higher success probabilities, whereas trials evaluating chemotherapy or assessing overall or event‐free survival endpoints showed lower success probabilities. Phase III trial success in oncology is associated with tumor‐specific characteristics and development‐stage factors, including biomarker‐based patient selection and trial design. These results provide quantitative evidence to inform decision‐making and trial design in oncology drug development. Study Highlights What is the current knowledge on the topic? ○Phase III oncology trials have a relatively low probability of success, and trial failure has been attributed to biological complexity, tumor heterogeneity, and challenges in late‐stage clinical development. However, relatively few studies have quantitatively characterized tumor‐specific success probabilities and comprehensively evaluated trial design‐related factors across oncological indications. What question did this study address? ○This study examined tumor‐specific success probabilities and quantitatively evaluated the factors associated with Phase III trial success in oncology drug development using publicly available trial data and multivariable logistic regression analysis. What does this study add to our knowledge? ○This study demonstrates substantial heterogeneity in Phase III trial success probabilities across tumor types and identifies key developmental stage factors associated with trial success, including biomarker‐based patient selection, therapeutic modality, line of therapy, and primary endpoint selection. How might this change clinical pharmacology or translational science? ○These findings provide quantitative evidence to inform decision‐making and trial design in late‐stage oncology drug development, supporting more rational and efficient development strategies for precision oncology.

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

Clinical trials for rare diseases face a fundamental mathematical challenge that conventional randomized controlled trial (RCT) designs cannot overcome. With approximately 95% of the estimated 10,000–16,000 rare diseases lacking approved therapies, and drug development programs failing at rates exceeding 75% in non‐oncology indications, the field confronts a stark reality: Traditional trial designs demand patient numbers that simply do not exist. This perspective article examines the critical mismatch between the statistical requirements of different trial designs (the “demand”) and the actual patient populations available for study (the “supply”). We demonstrate mathematically that alternative trial designs—particularly patient‐as‐own‐control and natural history comparator models—can reduce required sample sizes by 5‐ to 20‐fold while maintaining statistical rigor. We further point out that a substantial proportion of rare disease trial failures stem not from therapeutic inefficacy but from recruitment and retention challenges inherent to underpowered RCT designs—challenges that are directly addressable through appropriately matched trial design. Given that most rare disease development programs receive only one opportunity to demonstrate efficacy, the continued application of inappropriate statistical models represents both a scientific failure and an ethical and economic challenge to the rare disease community. We propose that regulatory agencies formalize acceptance of alternative trial designs for rare diseases, supported by explicit mathematical frameworks that transparently account for genetic heterogeneity, pediatric populations, and the statistical efficiency gains achieved through within‐subject correlation.

Positron emission tomography, an imaging tool using radiolabeled tracers in humans and preclinical species, has been widely used in recent years in drug development, particularly in the central nervous system. One important goal of PET in drug development is assessing the occupancy of various molecular targets (e.g., receptors, transporters, enzymes) by exogenous drugs. The current linear mathematical approaches used to determine occupancy using PET imaging experiments are presented. These algorithms use results from multiple regions with different target content in two scans, a baseline (pre-drug) scan and a post-drug scan. New mathematical estimation approaches to determine target occupancy, using maximum likelihood, are presented. A major challenge in these methods is the proper definition of the covariance matrix of the regional binding measures, accounting for different variance of the individual regional measures and their nonzero covariance, factors that have been ignored by conventional methods. The novel methods are compared to standard methods using simulation and real human occupancy data. The simulation data showed the expected reduction in variance and bias using the proper maximum likelihood methods, when the assumptions of the estimation method matched those in simulation. Between-method differences for data from human occupancy studies were less obvious, in part due to small dataset sizes. These maximum likelihood methods form the basis for development of improved PET covariance models, in order to minimize bias and variance in PET occupancy studies.

The goals of this article and special issue are to highlight the value of mathematical biology approaches in industry, help foster future interactions, and suggest ways for mathematics Ph.D. students and postdocs to move into industry careers. We include a candid examination of the advantages and challenges of doing mathematics in the biopharma industry, a broad overview of the types of mathematics being applied, information about academic collaborations, and career advice for those looking to move from academia to industry (including graduating Ph.D. students).