Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

How much do drug companies spend on research and development to bring a new medicine to market? In this study, which included 63 of 355 new therapeutic drugs and biologic agents approved by the US Food and Drug Administration between 2009 and 2018, the estimated median capitalized research and development cost per product was $985 million, counting expenditures on failed trials. Data were mainly accessible for smaller firms, products in certain therapeutic areas, orphan drugs, first-in-class drugs, therapeutic agents that received accelerated approval, and products approved between 2014 and 2018. This study provides an estimate of research and development costs for new therapeutic agents based on publicly available data; differences from previous studies may reflect the spectrum of products analyzed and the restricted availability of data in the public domain. The mean cost of developing a new drug has been the subject of debate, with recent estimates ranging from $314 million to $2.8 billion. To estimate the research and development investment required to bring a new therapeutic agent to market, using publicly available data. Data were analyzed on new therapeutic agents approved by the US Food and Drug Administration (FDA) between 2009 and 2018 to estimate the research and development expenditure required to bring a new medicine to market. Data were accessed from the US Securities and Exchange Commission, Drugs@FDA database, and ClinicalTrials.gov, alongside published data on clinical trial success rates. Conduct of preclinical and clinical studies of new therapeutic agents. Median and mean research and development spending on new therapeutic agents approved by the FDA, capitalized at a real cost of capital rate (the required rate of return for an investor) of 10.5% per year, with bootstrapped CIs. All amounts were reported in 2018 US dollars. The FDA approved 355 new drugs and biologics over the study period. Research and development expenditures were available for 63 (18%) products, developed by 47 different companies. After accounting for the costs of failed trials, the median capitalized research and development investment to bring a new drug to market was estimated at $985.3 million (95% CI, $683.6 million-$1228.9 million), and the mean investment was estimated at $1335.9 million (95% CI, $1042.5 million-$1637.5 million) in the base case analysis. Median estimates by therapeutic area (for areas with ≥5 drugs) ranged from $765.9 million (95% CI, $323.0 million-$1473.5 million) for nervous system agents to $2771.6 million (95% CI, $2051.8 million-$5366.2 million) for antineoplastic and immunomodulating agents. Data were mainly accessible for smaller firms, orphan drugs, products in certain therapeutic areas, first-in-class drugs, therapeutic agents that received accelerated approval, and products approved between 2014 and 2018. Results varied in sensitivity analyses using different estimates of clinical trial success rates, preclinical expenditures, and cost of capital. This study provides an estimate of research and development costs for new therapeutic agents based on publicly available data. Differences from previous studies may reflect the spectrum of products analyzed, the restricted availability of data in the public domain, and differences in underlying assumptions in the cost calculations. This study uses publicly available data to analyze research and development spending to win FDA approval and bring new drugs to market between 2009 and 2018.