Refining the impact of genetic evidence on clinical success.

The cost of drug discovery and development is driven primarily by failure1, with only about 10% of clinical programmes eventually receiving approval2-4. We previously estimated that human genetic evidence doubles the success rate from clinical development to approval5. In this study we leverage the growth in genetic evidence over the past decade to better understand the characteristics that distinguish clinical success and failure. We estimate the probability of success for drug mechanisms with genetic support is 2.6 times greater than those without. This relative success varies among therapy areas and development phases, and improves with increasing confidence in the causal gene, but is largely unaffected by genetic effect size, minor allele frequency or year of discovery. These results indicate we are far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Genetics in Drug Discovery.

Drug discovery is a complex process with high attrition rate: only about half of the compounds in advanced preclinical stages actually enter human trials. Key to these failures is our lack of understanding of human biology and the difficulties in translating our preclinical knowledge into cures. Here, we examine how genetics can be leveraged in drug discovery to understand and alter human biology.

Antibiotic Development: Lessons from the Past and Future Opportunities.

The challenge of antimicrobial resistance is broadly appreciated by the clinical and scientific communities. To assess progress in the development of medical countermeasures to combat bacterial infections, we deployed information gleaned from clinical trials conducted from 2000 to 2021. Whereas private sector interest in cancer grew dramatically over this period, activity to combat bacterial infections remained stagnant. The comparative ambivalence to antimicrobial resistance is reflected in the number of investigative drugs under clinical investigation, their stage of development and most troublingly, a declining number of organizations that are actively involved in the development of new products to treat bacterial infections. This drop reflects the exits of many companies that had previously developed antibacterial agents.

Challenges and recent progress in drug discovery for tropical diseases.

Infectious tropical diseases have a huge effect in terms of mortality and morbidity, and impose a heavy economic burden on affected countries. These diseases predominantly affect the world's poorest people. Currently available drugs are inadequate for the majority of these diseases, and there is an urgent need for new treatments. This Review discusses some of the challenges involved in developing new drugs to treat these diseases and highlights recent progress. While there have been notable successes, there is still a long way to go.

Therapeutic target database update 2022: facilitating drug discovery with enriched comparative data of targeted agents.

Drug discovery relies on the knowledge of not only drugs and targets, but also the comparative agents and targets. These include poor binders and non-binders for developing discovery tools, prodrugs for improved therapeutics, co-targets of therapeutic targets for multi-target strategies and off-target investigations, and the collective structure-activity and drug-likeness landscapes of enhanced drug feature. However, such valuable data are inadequately covered by the available databases. In this study, a major update of the Therapeutic Target Database, previously featured in NAR, was therefore introduced. This update includes (a) 34 861 poor binders and 12 683 non-binders of 1308 targets; (b) 534 prodrug-drug pairs for 121 targets; (c) 1127 co-targets of 672 targets regulated by 642 approved and 624 clinical trial drugs; (d) the collective structure-activity landscapes of 427 262 active agents of 1565 targets; (e) the profiles of drug-like properties of 33 598 agents of 1102 targets. Moreover, a variety of additional data and function are provided, which include the cross-links to the target structure in PDB and AlphaFold, 159 and 1658 newly emerged targets and drugs, and the advanced search function for multi-entry target sequences or drug structures. The database is accessible without login requirement at: https://idrblab.org/ttd/.

SwissBioisostere 2021: updated structural, bioactivity and physicochemical data delivered by a reshaped web interface.

At several stages of drug discovery, bioisosteric replacement is a common and efficient practice to find new bioactive chemotypes or to optimize series of molecules toward drug candidates. The critical steps consisting in selecting which molecular moiety should be replaced by which other chemical fragment is often relying on the expertise of specialists. Nowadays, valuable support can be obtained through the wealth of dedicated structural and knowledge data. The present article details the update of SwissBioisostere, a database of >25 millions of unique molecular replacements with data on bioactivity, physicochemistry, chemical and biological contexts extracted from the literature and related resources. The content of the database together with analysis and visualization capacities is freely available at www.swissbioisostere.ch.

The Promise of a Malaria Vaccine-Are We Closer?

Malaria vaccine development has rapidly advanced in the past decade. The very first phase 3 clinical trial of the RTS,S vaccine was completed with over 15,000 African infants and children, and pilot implementation studies are underway. Next-generation candidate vaccines using novel antigens, platforms, or approaches targeting different and/or multiple stages of the Plasmodium life cycle are being tested. Many candidates, in various stages of development, promise enhanced efficacy of long duration and broad protection against genetically diverse malaria strains, with a few studies under way in target populations in endemic areas. Malaria vaccines together with other interventions promise interruption and eventual elimination of malaria in endemic areas.

A Computational Pipeline to Predict Cardiotoxicity: From the Atom to the Rhythm.

RATIONALE: Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery. OBJECTIVE: To predict the impact of a drug from the drug chemistry on the cardiac rhythm. METHODS AND RESULTS: In a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction. CONCLUSIONS: We present a multiscale model framework to predict electrotoxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue-level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.

The genetics of drug efficacy: opportunities and challenges.

Lack of sufficient efficacy is the most common cause of attrition in late-phase drug development. It has long been envisioned that genetics could drive stratified drug development by identifying those patient subgroups that are most likely to respond. However, this vision has not been realized as only a small proportion of drugs have been found to have germline genetic predictors of efficacy with clinically meaningful effects, and so far all but one were found after drug approval. With the exception of oncology, systematic application of efficacy pharmacogenetics has not been integrated into drug discovery and development across the industry. Here, we argue for routine, early and cumulative screening for genetic predictors of efficacy, as an integrated component of clinical trial analysis. Such a strategy would identify clinically relevant predictors that may exist at the earliest possible opportunity, allow these predictors to be integrated into subsequent clinical development and provide mechanistic insights into drug disposition and patient-specific factors that influence response, therefore paving the way towards more personalized medicine.

Therapeutic approaches for SARS-CoV-2 infection.

Therapeutic approaches to COVID-19 treatment require appropriate inhibitors to target crucial proteins of SARS-CoV-2 replication machinery. It's been approximately 12 months since the pandemic started, yet no known specific drugs are available. However, research progresses with time in terms of high throughput virtual screening (HTVS) and rational design of repurposed, novel synthetic and natural products discovery by understanding the viral life cycle, immuno-pathological and clinical outcomes in patients based on host's nutritional, metabolic, and lifestyle status. Further, complementary and alternative medicine (CAM) approaches have also improved resiliency and immune responses. In this article, we summarize all the therapeutic antiviral strategies for COVID-19 drug discovery including computer aided virtual screening, repurposed drugs, immunomodulators, vaccines, plasma therapy, various adjunct therapies, and phage technology to unravel insightful mechanistic pathways of targeting SARS-CoV-2 and host's intrinsic, innate immunity at multiple checkpoints that aid in the containment of the disease.