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.

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.

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.

Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey.

Computational prediction of drug-target interactions (DTIs) has become an essential task in the drug discovery process. It narrows down the search space for interactions by suggesting potential interaction candidates for validation via wet-lab experiments that are well known to be expensive and time-consuming. In this article, we aim to provide a comprehensive overview and empirical evaluation on the computational DTI prediction techniques, to act as a guide and reference for our fellow researchers. Specifically, we first describe the data used in such computational DTI prediction efforts. We then categorize and elaborate the state-of-the-art methods for predicting DTIs. Next, an empirical comparison is performed to demonstrate the prediction performance of some representative methods under different scenarios. We also present interesting findings from our evaluation study, discussing the advantages and disadvantages of each method. Finally, we highlight potential avenues for further enhancement of DTI prediction performance as well as related research directions.

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.

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.

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.

Diversifying chemical libraries with generative topographic mapping.

Generative topographic mapping was used to investigate the possibility to diversify the in-house compounds collection of Boehringer Ingelheim (BI). For this purpose, a 2D map covering the relevant chemical space was trained, and the BI compound library was compared to the Aldrich-Market Select (AMS) database of more than 8M purchasable compounds. In order to discover new (sub)structures, the "AutoZoom" tool was developed and applied in order to analyze chemotypes of molecules residing in heavily populated zones of a map and to extract the corresponding maximum common substructures. A set of 401K new structures from the AMS database was retrieved and checked for drug-likeness and biological activity.

A comparison of stochastic programming methods for portfolio level decision-making.

Several methods have been presented in the literature for the management of a pharmaceutical portfolio, i.e. selecting which clinical studies should be conducted. We compare two existing approaches that use stochastic programming techniques and formulate the problem as a mixed integer linear programme (MILP). The first approach will be referred to as the ROV (real option valuation) approach since values are assigned to drug development programmes using methods for real option valuation. The second approach will be referred to as the PS (project scheduling) approach as this approach focusses on the scheduling of clinical studies and is formulated similarly to the resource constrained project scheduling problem. The ROV approach treats the value of a drug development programme as stochastic whereas the PS approach treats the trial outcomes as the stochastic component of the programme. As a consequence, the two approaches may select different portfolios. An advantage of the PS approach is that a schedule for when trials are to be conducted is provided as part of the optimal solution. This advantage comes at a much increased computational burden, however.

Innovative design and analysis for rare disease drug development.

One of the most challenges for rare diseases drug development is probably the availability of subjects with the diseases under a small patient population. It is then a great concern how to conduct clinical trials with the limited number of subjects available for obtaining substantial evidence regarding effectiveness and safety for approval of the drug product under investigation. For rare diseases drug development, FDA indicated that the Agency does not have the intention to create a statutory standard for approval of orphan drugs that is different from the standard for approval of drugs in common conditions. In this case, innovative thinking and approach for obtaining substantial evidence for approval of rare diseases drug products are necessarily applied. In this article, basic considerations for rare disease drug development are discussed. The innovative thinking of demonstrating not-ineffectiveness rather than effectiveness with a limited number of subjects available is outlined. In addition, an innovative approach utilizing a two-stage adaptive seamless trial design in conjunction with the concept of real-world data and real-world evidence is proposed not only to obtain substantial evidence for approval of rare diseases drug products, but also to meet the same standard as those drug products in common conditions. Under the two-stage adaptive seamless trial design, sample size calculation for rare diseases clinical trials based on the innovative probability monitoring procedure is also discussed.

Empirical profile Bayesian estimation for extrapolation of historical adult data to pediatric drug development.

For pediatric drug development, the clinical effectiveness of the study medication for the adult population has already been demonstrated. Given the fact that it is usually not feasible to enroll a large number of pediatric patients, appropriately leveraging historical adult data into pediatric evaluation may be critical to success of pediatric drug development. In this manuscript, we propose a new empirical Bayesian approach, profile Bayesian estimation, to dynamically borrow adult information to the evaluation of treatment effect in pediatric patients. The new approach demonstrates an attractive balance between type I error control and power gain under the transfer-ability assumption that the pediatric treatment effect size may differ from the adult treatment effect size. The decision making boundary mimics the real-world practice in pediatric drug development. In addition, the posterior mean of the proposed empirical profile Bayesian is an unbiased estimator of the true pediatric treatment effect. We compare our approach to robust mixture prior with prior weight for informative borrowing set to 0.5 or 0.9, regular Bayesian approach, and frequentist for both type I error and power.

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.

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.

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.

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.

Perspectives on the Role of Mathematics in Drug Discovery and Development.

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).

Modeling the Effect of Mucin Binding in the Gut on Drug Delivery.

An important part the absorption, distribution, metabolism and excretion of an oral therapeutic is the flux rate of drug compound crossing the mucus lining of the gut. To understand this part of the absorption process, we develop a mathematical model of advection, diffusion and binding of drug compounds within the mucus layer of the intestines. Analysis of this model yields simple, measurable criteria for the successful mucin layer traversal of drug compound.

Quantitative PET Imaging in Drug Development: Estimation of Target Occupancy.

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.

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.

Probability monitoring procedures for sample size determination.

In clinical research, power analysis is often performed for sample size calculation. The purpose is to achieve a desired power of correctly detecting a clinically meaningful difference at a pre-specified level of significance if such a difference truly exists. However, in some situations such as (i) clinical trials with extremely low incidence rates and (ii) for rare disease drug development clinical trials, power analysis for sample size calculation may not be feasible because (i) it may require a huge sample size for detecting a relatively small difference and (ii) eligible patients may not be available for a small target patient population. In these cases, other procedures for sample size determination with certain statistical assurance are needed. In this article, an innovative method based on a probability monitoring procedure is proposed for sample size determination. The concept is to select an appropriate sample size for controlling the probability of crossing safety and/or efficacy boundaries. For rare disease clinical development, an adaptive probability monitoring procedure may be applied if a multiple-stage adaptive trial design is used.