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.

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.

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.

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.

Drug development is a lengthy and complicated process, which requires costly experiments on a very large number of chemical compounds. The identification of chemical molecules with desired properties can be facilitated by computational methods. Several methods were developed for computer-aided design of drugs that target protein molecules. However, recently the ribonucleic acid (RNA) emerged as an attractive target for the development of new drugs. Unfortunately, the portfolio of the computer methods that can be applied to study RNA and its interactions with small chemical molecules is very limited. This situation motivated us to develop a new computational method, with which to predict RNA-small molecule interactions. To this end, we collected the information on the statistics of interactions in experimentally determined structures of complexes formed by RNA with small molecules. We then used the statistical data to train machine learning methods aiming to distinguish between RNA-ligand interactions observed experimentally and other interactions that can be observed in theoretical analyses, but are not observed in nature. The resulting method called AnnapuRNA is superior to other similar tools and can be used to predict preferred ligands of RNA molecules and how RNA and small molecules interact with each other.

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.

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.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

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.

PathFX provides mechanistic insights into drug efficacy and safety for regulatory review and therapeutic development

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.

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.

A proposal for a new PhD level curriculum on quantitative methods for drug development

This paper provides an overview of "Improving Design, Evaluation and Analysis of early drug development Studies" (IDEAS), a European Commission-funded network bringing together leading academic institutions and small- to large-sized pharmaceutical companies to train a cohort of graduate-level medical statisticians. The network is composed of a diverse mix of public and private sector partners spread across Europe, which will host 14 early-stage researchers for 36 months. IDEAS training activities are composed of a well-rounded mixture of specialist methodological components and generic transferable skills. Particular attention is paid to fostering collaborations between researchers and supervisors, which span academia and the private sector. Within this paper, we review existing medical statistics programmes (MSc and PhD) and highlight the training they provide on skills relevant to drug development. Motivated by this review and our experiences with the IDEAS project, we propose a concept for a joint, harmonised European PhD programme to train statisticians in quantitative methods for drug development.

Periodic benefit-risk assessment using Bayesian stochastic multi-criteria acceptability analysis

Benefit-risk (BR) assessment is essential to ensure the best decisions are made for a medical product in the clinical development process, regulatory marketing authorization, post-market surveillance, and coverage and reimbursement decisions. One challenge of BR assessment in practice is that the benefit and risk profile may keep evolving while new evidence is accumulating. Regulators and the International Conference on Harmonization (ICH) recommend performing periodic benefit-risk evaluation report (PBRER) through the product's lifecycle. In this paper, we propose a general statistical framework for periodic benefit-risk assessment, in which Bayesian meta-analysis and stochastic multi-criteria acceptability analysis (SMAA) will be combined to synthesize the accumulating evidence. The proposed approach allows us to compare the acceptability of different drugs dynamically and effectively and accounts for the uncertainty of clinical measurements and imprecise or incomplete preference information of decision makers. We apply our approaches to two real examples in a post-hoc way for illustration purpose. The proposed method may easily be modified for other pre and post market settings, and thus be an important complement to the current structured benefit-risk assessment (sBRA) framework to improve the transparent and consistency of the decision-making process.