From target discovery to clinical drug development with human genetics.

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.

Prediction of New Risk Genes and Potential Drugs for Rheumatoid Arthritis from Multiomics Data.

Rheumatoid arthritis (RA) is an autoimmune and inflammatory disease for which there is a lack of therapeutic options. Genome-wide association studies (GWASs) have identified over 100 genetic loci associated with RA susceptibility; however, the most causal risk genes (RGs) associated with, and molecular mechanism underlying, RA remain unknown. In this study, we collected 95 RA-associated loci from multiple GWASs and detected 87 candidate high-confidence risk genes (HRGs) from these loci via integrated multiomics data (the genome-scale chromosome conformation capture data, enhancer-promoter linkage data, and gene expression data) using the Bayesian integrative risk gene selector (iRIGS). Analysis of these HRGs indicates that these genes were indeed, markedly associated with different aspects of RA. Among these, 36 and 46 HRGs have been reported to be related to RA and autoimmunity, respectively. Meanwhile, most novel HRGs were also involved in the significantly enriched RA-related biological functions and pathways. Furthermore, drug repositioning prediction of the HRGs revealed three potential targets (ERBB2, IL6ST, and MAPK1) and nine possible drugs for RA treatment, of which two IL-6 receptor antagonists (tocilizumab and sarilumab) have been approved for RA treatment and four drugs (trastuzumab, lapatinib, masoprocol, and arsenic trioxide) have been reported to have a high potential to ameliorate RA. In summary, we believe that this study provides new clues for understanding the pathogenesis of RA and is important for research regarding the mechanisms underlying RA and the development of therapeutics for this condition.

Use of Real-World Data and Evidence in Drug Development of Medicinal Products Centrally Authorized in Europe in 2018-2019.

Real-world data/real-world evidence (RWD/RWE) are considered to have a great potential to complement, in some cases, replace the evidence generated through randomized controlled trials. By tradition, use of RWD/RWE in the postauthorization phase is well-known, whereas published evidence of use in the pre-authorization phase of medicines development is lacking. The primary aim of this study was to identify and quantify the role of potential use of RWD/RWE (RWE signatures) during the pre-authorization phase, as presented in the initial marketing authorization applications of new medicines centrally evaluated with a positive opinion in 2018-2019 (n = 111) by the European Medicines Agency (EMA). Data for the study was retrieved from the evaluation overviews of the European Public Assessment Reports (EPARs), which reflect the scientific conclusions of the assessment process and are accessible through the EMA website. RWE signatures were extracted into an RWE Data Matrix, including 11 categories divided over 5 stages of the drug development lifecycle. Nearly all EPARs included RWE signatures for the discovery (98.2%) and life-cycle management (100.0%). Half of them included RWE signatures for the full development phase (48.6%) and for supporting regulatory decisions at the registration (46.8%), whereas over a third (35.1%) included RWE signatures for the early development. RWE signatures were more often seen for orphan and conditionally approved medicines. Oncology, hematology, and anti-infectives stood out as therapeutic areas with most RWE signatures in their full development phase. The findings bring unprecedented insights about the vast use of RWD/RWE in drug development supporting the regulatory decision making.

The 2020 race towards SARS-CoV-2 specific vaccines.

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.

Exploratory Analysis of the Factors Associated With Success Rates of Confirmatory Randomized Controlled Trials in Cancer Drug Development.

This study examined the outcomes of recent confirmatory randomized controlled trials (RCTs) in phase III that were initiated between 2005 and 2017 for oncologic drugs in the United States and identified several factors that were associated with the success of RCTs. Our regression analysis showed that studies with progression-free survival or response rate as primary end point were more likely to succeed than studies with overall survival (odds ratio (OR) = 2.94 and 6.23, respectively). The status of development was also linked with success rates. Studies for non-lead indication tended to have lower success rates than studies for lead indication (OR = 0.68). Studies for first-line therapy were observed to have low success rates compared with studies for post second-line therapies (OR = 0.37). Studies for which strong prior evidence was not listed in their publication tended to be more successful than studies that followed rigorous RCTs or single arm studies for the indication. These results suggest that historical success rates may reflect not only the important features of trials, which can be observed directly from study design and results, but also the background status of trials in clinical development pathways.

Impact of COVID-19 in paediatric early-phase cancer clinical trials in Europe: A report from the Innovative Therapies for Children with Cancer (ITCC) consortium.

INTRODUCTION: Data regarding real-world impact on cancer clinical research during COVID-19 are scarce. We analysed the impact of the COVID-19 pandemic on the conduct of paediatric cancer phase I-II trials in Europe through the experience of the Innovative Therapies for Children with Cancer (ITCC). METHODS: A survey was sent to all ITCC-accredited early-phase clinical trial hospitals including questions about impact on staff activities, recruitment, patient care, supply of investigational products and legal aspects, between 1st March and 30th April 2020. RESULTS: Thirty-one of 53 hospitals from 12 countries participated. Challenges reported included staff constraints (30% drop), reduction in planned monitoring activity (67% drop of site initiation visits and 64% of monitoring visits) and patient recruitment (61% drop compared with that in 2019). The percentage of phase I, phase II trials and molecular platforms closing to recruitment in at least one site was 48.5%, 61.3% and 64.3%, respectively. In addition, 26% of sites had restrictions on performing trial assessments because of local contingency plans. Almost half of the units suffered impact upon pending contracts. Most hospitals (65%) are planning on improving organisational and structural changes. CONCLUSION: The study reveals a profound disruption of paediatric cancer early-phase clinical research due to the COVID-19 pandemic across Europe. Reported difficulties affected both patient care and monitoring activity. Efforts should be made to reallocate resources to avoid lost opportunities for patients and to allow the continued advancement of oncology research. Identified adaptations to clinical trial procedures may be integrated to increase preparedness of clinical research to futures crises.

Optimal decision-making in oncology development programs based on probability of success for phase III utilizing phase II/III data on response and overall survival.

In clinical development, there is a trade-off between investment and level of confidence in the potential of the drug before going into phase III. Reduced investment requires the use of short-term endpoints. On new compounds, only limited information about the relationship between treatment effects of short- and long-term endpoints is usually available. Therefore, decision-making solely based on short-term endpoints does not seem desirable. Our goal is to plan an efficient development program, which uses short- and long-term endpoints data for decision-making. We found that with limited prior information and restrictions on maximum sample size, decision-making after phase II cannot be substantially improved. We follow the concept of a "phase 2+" design where after a go-to-phase-III-decision, further follow-up data from phase II are employed to make interim decisions on phase III. The program will be stopped early when additional phase II and/or available phase III data lead to a low probability of success (PoS). We utilize information from a multi-categorical short-term endpoint (response status) and a long-term endpoint (overall survival (OS)) to determine the PoS in phase III with OS as the primary endpoint. Optimal combinations of decision boundaries and time points are demonstrated in a simulation study. Our results show that the proposed second look using additional follow-up data from phase II/III improves PoS estimates compared to the first look, especially when prior data about the control arm is available. The proposed planning strategy allows a customized compromise between the quality of decision-making and program duration.

Proportion of Patients in Phase I Oncology Trials Receiving Treatments That Are Ultimately Approved.

BACKGROUND: Phase I oncology trials are often regarded as a therapeutic option for patients. However, such claims have relied on surrogate measures of benefit, such as objective response. METHODS: Using a systematic search of publications, we assessed the therapeutic value of phase I cancer trial participation by determining the probability that patients will receive active doses of treatments that eventually receive FDA approval or a National Comprehensive Cancer Network (NCCN) guideline recommendation for their indication. ClinicalTrials.gov, PubMed, American Society of Clinical Oncology reports, NCCN guidelines, and Drugs@FDA were searched between May 1, 2018, and July 31, 2018. All statistical tests were 2-sided. RESULTS: A total of 1000 phase I oncology trials initiated between 2005 and 2010 and enrolling 32 582 patients were randomly sampled from 3229 eligible trials on ClinicalTrials.gov. A total of 386 (1.2%) patients received a treatment that was approved by the US Food and Drug Administration for their malignancy at a dose delivered in the trial; including NCCN guideline recommendations, the number and proportion are 1168 (3.6%). Meta-regression showed a statistically significantly greater proportion of patients receiving a drug that was ultimately FDA approved in biomarker trials (rate ratio = 4.49, 95% confidence interval [CI] = 1.53 to 13.23; P = .006) and single-indication trials (rate ratio = 3.32, 95% CI = 1.21 to 9.15; P = .02); proportions were statistically significantly lower for combination vs monotherapy trials (rate ratio = 0.09, 95% CI = 0.01 to 0.68; P = .02). CONCLUSIONS: One in 83 patients in phase I cancer trials received a treatment that was approved for their indication at the doses received. Given published estimates of serious adverse event rates of 10%-19%, this represents low therapeutic value for phase I trial participation.

Comparison of US Federal and Foundation Funding of Research for Sickle Cell Disease and Cystic Fibrosis and Factors Associated With Research Productivity.

Importance: 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. Objective: To compare disease-specific funding between SCD and CF and the association between funding and research productivity. Design, Setting, and Participants: 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. Main Outcomes and Measures: Federal and foundation funding, publications indexed in PubMed, clinical trial registrations, and new drug approvals. Results: 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). Conclusions and Relevance: 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.

Integration of Omics Data Sources to Inform Mechanistic Modeling of Immune-Oncology Therapies: A Tutorial for Clinical Pharmacologists.

Application of contemporary molecular biology techniques to clinical samples in oncology resulted in the accumulation of unprecedented experimental data. These "omics" data are mined for discovery of therapeutic target combinations and diagnostic biomarkers. It is less appreciated that omics resources could also revolutionize development of the mechanistic models informing clinical pharmacology quantitative decisions about dose amount, timing, and sequence. We discuss the integration of omics data to inform mechanistic models supporting drug development in immuno-oncology. To illustrate our arguments, we present a minimal clinical model of the Cancer Immunity Cycle (CIC), calibrated for non-small cell lung carcinoma using tumor microenvironment composition inferred from transcriptomics of clinical samples. We review omics data resources, which can be integrated to parameterize mechanistic models of the CIC. We propose that virtual trial simulations with clinical Quantitative Systems Pharmacology platforms informed by omics data will be making increasing impact in the development of cancer immunotherapies.

Confidence, prediction, and tolerance in linear mixed models.

The literature about Prediction Interval (PI) and Tolerance Interval (TI) in linear mixed models is usually developed for specific designs, which is a main limitation to their use. This paper proposes to reformulate the two-sided PI to be generalizable under a wide variety of designs (one random factor, nested and crossed designs for multiple random factors, and balanced or unbalanced designs). This new methodology is based on the Hessian matrix, namely, the inverse of (observed) Fisher Information matrix, and is built with a cell mean model. The degrees of freedom for the total variance are calculated with the generalized Satterthwaite method and compared to the Kenward-Roger's degrees of freedom for fixed effects. Construction of two-sided TIs are also detailed with one random factor, and two nested and two crossed random variables. An extensive simulation study is carried out to compare the widths and coverage probabilities of Confidence Intervals (CI), PIs, and TIs to their nominal levels. It shows excellent coverage whatever the design and the sample size are. Finally, these CIs, PIs, and TIs are applied to two real data sets: one from orthopedic surgery study (intralesional resection risk) and the other from assay validation study during vaccine development.

Romiplostim for the management of pediatric immune thrombocytopenia: drug development and current practice.

Since successful cloning of thrombopoietin (TPO) in 1994, significant advances have been made in the development of recombinant TPO receptor agonists. The US Food and Drug Administration (FDA) has approved 2 agents for use in patients with immune thrombocytopenia (ITP): eltrombopag and romiplostim. Romiplostim is a once-weekly subcutaneous injection that has been shown to increase the platelet count, lessen bleeding, and reduce concurrent medication use in adults with ITP. In December 2018, the US FDA approved romiplostim for use in pediatric patients ≥1 year of age with ITP of >6 months' duration and insufficient response to corticosteroids, immunoglobulins, or splenectomy, based on similarly favorable clinical trial data. In addition, romiplostim is well tolerated, making it an attractive option for the treatment of children. Expansion of off-label romiplostim use is being reported in children for ITP <6 months, neonatal thrombocytopenia, hereditary thrombocytopenias, and chemotherapy- and bone marrow transplant-associated thrombocytopenia. We review here the development of romiplostim with a focus on pediatric use.

Variability of Betweenness Centrality and Its Effect on Identifying Essential Genes.

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.

Diversifying clinical trials.

Scientific common sense and social justice dictate that the safety and efficacy of new therapies must be tested in the patient populations in need of treatment. Yet a recent study found that African Americans have been dramatically underrepresented in US clinical trials for cancer drugs. Efforts to increase the participation of minorities in clinical trials must become a priority for all drug developers.

The use of real-world data in cancer drug development.

Excitement about the dramatic increase in potential successful anticancer medicines in recent years is hampered by the high costs involved as well as the length of time traditional pathways take for regulatory approval. The translation of experimental clinical data into real-world evidence is also problematic. While the randomised controlled trial remains the gold standard for assessing efficacy and safety, there is increasing interest in the use of observational data to enable more rapid, informed and widespread availability and access to important anticancer medicines. Taking real-world evidence into account in regulatory and health technology assessment in a thoughtful and balanced fashion will enrich and justify sound decision-making.

Flexible alpha allocation strategies for confirmatory adaptive enrichment clinical trials with a prespecified subgroup.

Adaptive enrichment designs have recently received considerable attention as they have the potential to make drug development process for personalized medicine more efficient. Several statistical approaches have been proposed so far in the literature and the operating characteristics of these approaches are extensively investigated using simulation studies. In this paper, we improve on existing adaptive enrichment designs by assigning unequal weights to the significance levels associated with the hypotheses of the overall population and a prespecified subgroup. More specifically, we focus on the standard combination test, a modified combination test, the marginal combination test, and the partial conditional error rate approach and explore the operating characteristics of these approaches by a simulation study. We show that these approaches can lead to power gains, compared to existing approaches, if the weights are chosen carefully.

Trends of Clinical Trials for Drug Development in Rare Diseases.

BACKGROUND: Drug development for rare diseases is challenging because it is difficult to obtain relevant data from very few patients. It must be informative to grasp current status of clinical trials for drug development in rare diseases. OBJECTIVE: Clinical trials in rare diseases are to be outlined and compared among the US, EU and Japan. METHOD: ClinicalTrials.gov (NCT, National Clinical Trial), EU Clinical Trials Register (EUCTR) and the Japan Primary Registries Network (JPRN) were analyzed. Clinical trials involving information on rare diseases and drugs were extracted by text-mining, based on the diseases and drugs derived from Orphanet and DrugBank, respectively. RESULTS: In total, 28,526 clinical trials were extracted, which studied 1,535 rare diseases and 1,539 drugs. NCT had the largest number of trials, involving 1,252 diseases and 1,332 drugs. EUCTR and JPRN also had registry-specific diseases (250 and 22, respectively) and drugs (172 and 29, respectively) that should not be missed. Among the 1,535 rare diseases, most diseases were studied in only a limited number of trials; 70% of diseases were studied in fewer than 10 trials, and 28% were studied in only one. Additionally, most studied rare diseases were cancer-related ones. CONCLUSION: This study has revealed the characteristics of the clinical trials in rare diseases among the US, EU and Japan. The number of trials for rare diseases was limited especially for non-cancerrelated ones. This information could contribute to drug development such as drug-repositioning in rare diseases.

The impact of parallel regulatory-health technology assessment scientific advice on clinical development. Assessing the uptake of regulatory and health technology assessment recommendations.

AIMS: The parallel regulatory-health technology assessment scientific advice (PSA) procedure allows manufacturers to receive simultaneous feedback from both EU regulators and health technology assessment (HTA) bodies on development plans for new medicines. The primary objective of the present study is to investigate whether PSA is integrated in the clinical development programmes for which advice was sought. METHODS: Contents of PSA provided by regulators and HTA bodies for each procedure between 2010 and 2015 were analysed. The development of all clinical studies for which PSA had been sought was tracked using three different databases. The rate of uptake of the advice provided by regulators and HTA bodies was assessed on two key variables: comparator/s and primary endpoint. RESULTS: In terms of uptake of comparator recommendations at the time of PSA in the actual development, our analysis showed that manufacturers implemented comparators to address both the needs of regulators and of at least one HTA body in 12 of 21 studies. For primary endpoints, in all included studies manufacturers addressed both the needs of the regulators and at least one HTA body. CONCLUSIONS: One of the key findings of this analysis is that manufacturers tend to implement changes to the development programme based on both regulatory and HTA advice with regards to the choice of primary endpoint and comparator. It also confirms the challenging choice of the study comparator, for which manufacturers seem to be more inclined to satisfy the regulatory advice. Continuous research efforts in this area are of paramount importance from a public health perspective.

Some statistical considerations in the clinical development of cancer immunotherapies.

Immuno-oncology has emerged as an exciting new approach to cancer treatment. Common immunotherapy approaches include cancer vaccine, effector cell therapy, and T-cell-stimulating antibody. Checkpoint inhibitors such as cytotoxic T lymphocyte-associated antigen 4 and programmed death-1/L1 antagonists have shown promising results in multiple indications in solid tumors and hematology. However, the mechanisms of action of these novel drugs pose unique statistical challenges in the accurate evaluation of clinical safety and efficacy, including late-onset toxicity, dose optimization, evaluation of combination agents, pseudoprogression, and delayed and lasting clinical activity. Traditional statistical methods may not be the most accurate or efficient. It is highly desirable to develop the most suitable statistical methodologies and tools to efficiently investigate cancer immunotherapies. In this paper, we summarize these issues and discuss alternative methods to meet the challenges in the clinical development of these novel agents. For safety evaluation and dose-finding trials, we recommend the use of a time-to-event model-based design to handle late toxicities, a simple 3-step procedure for dose optimization, and flexible rule-based or model-based designs for combination agents. For efficacy evaluation, we discuss alternative endpoints/designs/tests including the time-specific probability endpoint, the restricted mean survival time, the generalized pairwise comparison method, the immune-related response criteria, and the weighted log-rank or weighted Kaplan-Meier test. The benefits and limitations of these methods are discussed, and some recommendations are provided for applied researchers to implement these methods in clinical practice.