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.

Quantitative systems pharmacology: Landscape analysis of regulatory submissions to the US Food and Drug Administration.

Quantitative systems pharmacology (QSP) has been proposed as a scientific domain that can enable efficient and informative drug development. During the past several years, there has been a notable increase in the number of regulatory submissions that contain QSP, including Investigational New Drug Applications (INDs), New Drug Applications (NDAs), and Biologics License Applications (BLAs) to the US Food and Drug Administration. However, there has been no comprehensive characterization of the nature of these regulatory submissions regarding model details and intended applications. To address this gap, a landscape analysis of all the QSP submissions as of December 2020 was conducted. This report summarizes the (1) yearly trend of submissions, (2) proportion of submissions between INDs and NDAs/BLAs, (3) percentage distribution along the stages of drug development, (4) percentage distribution across various therapeutic areas, and (5) nature of QSP applications. In brief, QSP is increasingly applied to model and simulate both drug effectiveness and safety throughout the drug development process across disease areas.

A Review of the Main Considerations for Formulation Development in Preclinical Toxicology Studies.

The main considerations for the development of a formulation for preclinical safety assessment testing are explored. Intravenous, inhalation, oral and dermal dosing are given focus and although different dose routes do present their own individual challenges there are common themes that emerge. In each case it is necessary to maximise exposure to achieve high doses to satisfy regulatory requirements for safety assessment testing. This often involves producing formulations that are at the limits of solubility and maximum volumes possible for administration to different test species by the chosen route. It is concluded that for all routes it is important to thoroughly explore the stability of the test item in the proposed formulation matrix well ahead of dosing any animals, giving careful consideration to which excipients are used and what their underlying toxicity profile may be for the relevant preclinical species. In addition, determining the maximum achievable concentrations and weighing that against the maximum volumes that can be given by the chosen route in all the test species at an early stage will also give a read on whether it would be theoretically possible to achieve suitably high enough doses to support clinical work. Not doing so can cause delays in the development programme and may have ethical repercussions.

Women and babies are dying from inertia: a collaborative framework for obstetrical drug development is urgently needed.

Obstetrical complications, often referred to as the "great obstetrical syndromes," are among the most common global causes of mortality and morbidity in young women and their infants. However, treatments for these syndromes are underdeveloped compared with other fields of medicine and are urgently needed. This current paucity of treatments for obstetrical complications is a reflection of the challenges of drug development in pregnancy. The appetite of pharmaceutical companies to invest in research for obstetrical syndromes is generally reduced by concerns for maternal, fetal, and infant safety, poor definition, and high-risk regulatory paths toward product approval. Notably, drug candidates require large investments for development with an unguaranteed return on investment. Furthermore, the discovery of promising drug candidates is hampered by a poor understanding of the pathophysiology of obstetrical syndromes and their uniqueness to human pregnancies. This limits translational extrapolation and de-risking strategies in preclinical studies, as available for other medical areas, compounded with limited fetal safety monitoring to capture early prenatal adverse reactions. In addition, the ethical review committees are reluctant to approve the inclusion of pregnant women in trials, and in the absence of regulatory guidance in obstetrics, clinical development programs are subject to unpredictable regulatory paths. To develop effective and safe drugs for pregnancy complications, substantial commitment, and investment in research for innovative therapies are needed in parallel with the creation of an enabling ethical, legislative, and guidance framework. Solutions are proposed to enable stakeholders to work with a common set of expectations to facilitate progress in this medical discipline. Addressing this significant unmet need to advance maternal and possibly perinatal health requires the involvement of all stakeholders and specifically patients, couples, and clinicians facing pregnancy complications in the dearth of appropriate therapies. This paper focused on the key pharmaceutical research and development challenges to achieve effective and safe treatments for obstetrical syndromes.

The need to show minimum clinically important differences in Alzheimer's disease trials.

Deciding on the smallest change in an outcome that constitutes a clinically meaningful treatment effect (ie, the minimum clinically important difference [MCID]) is fundamental to interpreting clinical trial outcomes, making clinical decisions, and designing studies with sufficient statistical power to detect any such effect. There is no consensus on MCIDs for outcomes in Alzheimer's disease trials, but the US Food and Drug Administration's consideration of aducanumab clinical trials data has exposed the uncertainty of the clinical meaning of statistically significant but small improvements. Although MCIDs for outcomes, including Clinical Dementia Rating-Sum of Boxes and Mini-Mental State Examination in Alzheimer's disease have been reported, the Food and Drug Administration's guidelines, drafted in 1989 to facilitate regulatory approval of substantially effective antidementia drugs, do not specify quantified minimum differences. Although it is important that regulatory requirements encourage drug development and approval, without MCIDs, sponsors are motivated to power trials to detect statistical significance for only small and potentially inconsequential effects on clinical outcomes. MCIDs benefit patients, family members, caregivers, and health-care systems and should be incorporated into clinical trials and drug development guidance for Alzheimer's disease.

LUNAR :Drug Screening for Novel Coronavirus Based on Representation Learning Graph Convolutional Network.

An outbreak of COVID-19 that began in late 2019 was caused by a novel coronavirus(SARS-CoV-2). It has become a global pandemic. As of June 9, 2020, it has infected nearly 7 million people and killed more than 400,000, but there is no specific drug. Therefore, there is an urgent need to find or develop more drugs to suppress the virus. Here, we propose a new nonlinear end-to-end model called LUNAR. It uses graph convolutional neural networks to automatically learn the neighborhood information of complex heterogeneous relational networks and combines the attention mechanism to reflect the importance of the sum of different types of neighborhood information to obtain the representation characteristics of each node. Finally, through the topology reconstruction process, the feature representations of drugs and targets are forcibly extracted to match the observed network as much as possible. Through this reconstruction process, we obtain the strength of the relationship between different nodes and predict drug candidates that may affect the treatment of COVID-19 based on the known targets of COVID-19. These selected candidate drugs can be used as a reference for experimental scientists and accelerate the speed of drug development. LUNAR can well integrate various topological structure information in heterogeneous networks, and skillfully combine attention mechanisms to reflect the importance of neighborhood information of different types of nodes, improving the interpretability of the model. The area under the curve(AUC) of the model is 0.949 and the accurate recall curve (AUPR) is 0.866 using 10-fold cross-validation. These two performance indexes show that the model has superior predictive performance. Besides, some of the drugs screened out by our model have appeared in some clinical studies to further illustrate the effectiveness of the model.

R&D efficiency of leading pharmaceutical companies - A 20-year analysis.

Comparative analysis of the R&D efficiency of 14 leading pharmaceutical companies for the years 1999-2018 shows that there is a close positive correlation between R&D spending and the two investigated R&D output parameters, approved NMEs and the cumulative impact factor of their publications. In other words, higher R&D investments (input) were associated with higher R&D output. Second, our analyses indicate that there are 'economies of scale' (size) in pharmaceutical R&D.

Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development.

INTRODUCTION: The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks. METHODS: In this context, we pre-sent a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time. RESULTS: Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with clinicaltrials.gov and PubMed in the field of pharmacology and clinical intervention. DISCUSSION: The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic.

Changes in clinical trials of endocrine disorder and metabolism and nutrition disorder drugs in mainland China over 2010-2019.

With the improvements in relevant policies, laws, and regulations regarding drug clinical trials in China, the quantity and quality of drug clinical trials have gradually improved, and the development prospects of drug clinical trials for endocrine disorders and metabolism and nutrition disorders are promising. Based on information from the clinical trials from the online drug clinical trial registration platform of the National Medical Products Administration, we aimed to review and evaluate the development of clinical trials of drugs for endocrine disorders and metabolism and nutrition disorders in mainland China from 2010 to 2019, as well as the trends over time. A total of 861 trials were carried out on 254 types of drugs for endocrine disorders and metabolism and nutrition disorders, among which 531 (61.67%) involved endocrine disorders, and 330 (38.33%) addressed metabolism and nutrition disorders. The annual number of clinical trials has been increasing gradually, with a significant increase in 2017. Among them, the proportion of clinical trials with Chinese epidemiological characteristics was relatively large (Wu, Annual Report on Development Health Management and Health Industry in China, 2018). The largest number of trials were for diabetes drugs (55.63%), followed by trials of drugs for hyperlipidemia (19.4%) and those for hyperuricemia (7.9%). It was found that the geographical area of the leading units also showed obvious unevenness according to the analysis of the test unit data. Based on the statistics and evaluation of the data, comprehensive information is provided to support the cooperation of global pharmaceutical R&D companies and research units in China and the development of international multicenter clinical trials in China. This work additionally provides clinical trial units with a self-evaluation of scientific research competitiveness and hospital development strategies. At the same time, it provides a reference with basic data for sponsors and stakeholders in these trials to determine their development strategy goals.

AnnapuRNA: A scoring function for predicting RNA-small molecule binding poses.

RNA is considered as an attractive target for new small molecule drugs. Designing active compounds can be facilitated by computational modeling. Most of the available tools developed for these prediction purposes, such as molecular docking or scoring functions, are parametrized for protein targets. The performance of these methods, when applied to RNA-ligand systems, is insufficient. To overcome these problems, we developed AnnapuRNA, a new knowledge-based scoring function designed to evaluate RNA-ligand complex structures, generated by any computational docking method. We also evaluated three main factors that may influence the structure prediction, i.e., the starting conformer of a ligand, the docking program, and the scoring function used. We applied the AnnapuRNA method for a post-hoc study of the recently published structures of the FMN riboswitch. Software is available at https://github.com/filipspl/AnnapuRNA.

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.

Clinical Development Times for Biosimilars in the United States.

Biosimilars are versions of biologic drugs made by different manufacturers that can help lower spending by promoting competition. However, few biosimilars are currently available in the US. To assess the role of testing requirements in this outcome, we investigated clinical development times for 40 biosimilars that initiated phase I testing between 2012 and 2015. We found that most biosimilars underwent phase III testing with an average trial length of 22 months. Of 20 biosimilars that had been approved by October 2019, the median time from initiation of phase I testing to approval was 69.9 months. These findings reveal a high testing bar for approval that likely contributed to limited market entry.

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.

Pharmacokinetic/pharmacodynamic data extrapolation models for improved pediatric efficacy and toxicity estimation, with application to secondary hyperparathyroidism.

In most drug development settings, the regulatory approval process is accompanied by extensive studies performed to understand the drug's pharmacokinetic (PK) and pharmacodynamic (PD) properties. In this article, we attempt to utilize the rich PK/PD data to inform the borrowing of information from adults during pediatric drug development. In pediatric settings, it is especially crucial that we are parsimonious with the patients recruited for experimentation. We illustrate our approaches in the context of clinical trials of cinacalcet for treating secondary hyperparathyroidism in pediatric and adult patients with chronic kidney disease, where we model both parathyroid hormone (efficacy endpoint) and corrected calcium levels (safety endpoint). We use population PK/PD modeling of the cinacalcet data to quantitatively assess the similarity between adults and children, and use this information in various hierarchical Bayesian adult borrowing rules whose statistical properties can then be evaluated. In particular, we simulate the bias and mean square error performance of our approaches in settings where borrowing is and is not warranted to inform guidelines for the future use of our methods.

Parameter Estimation and Reporting in Noncompartmental Analysis of Clinical Pharmacokinetic Data.

The noncompartmental analysis (NCA) of pharmacokinetic (PK) data is important throughout all phases of clinical drug development. Although there are numerous regulatory guidances and articles in the literature concerned with best practices for the modeling of PK data, there are relatively few sources of information on how to conduct a high-quality NCA. This article provides a systematic review of issues related to the estimation of plasma and urine PK parameters with the intent of encouraging rigor in the performance of NCAs so as to provide reliable and informative analysis results.