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Antibiotic resistance is undoubtedly one of the greatest challenges to global health, and the emergence of resistance has outpaced the development of new antibiotics. However, investments by the pharmaceutical industry and biotechnology companies for research into and development of new antibiotics are diminishing. The public health implications of a drying antibiotic pipeline are recognized by policymakers, regulators and many companies. In this Viewpoint article, seven experts discuss the challenges that are contributing to the decline in antibiotic drug discovery and development, and the national and international initiatives aimed at incentivizing research and the development of new antibiotics to improve the economic feasibility of antibiotic development.In this Viewpoint article, seven experts discuss the challenges that are contributing to the decline in antibiotic drug discovery and development, and the international and national initiatives aimed at incentivizing research and the development of new antibiotics to improve the economic feasibility of antibiotic development.

Key Points This Analysis article describes the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The analysis reaffirms that control of physicochemical properties during compound optimization is beneficial in identifying compounds of candidate drug quality. Safety and toxicology are the largest sources of failure within the data set. The link between calculated physicochemical properties and frequent causes of attrition (preclinical toxicology, clinical safety and human pharmacokinetics) is assessed. Analysis of this data set shows that none of the physicochemical descriptors we examined correlates with preclinical toxicology outcomes. This work is the first to indicate a link between lipophilicity and clinical failure owing to safety issues. The utility of this finding in a prospective sense is discussed. Although control of physicochemical properties is clearly important, this analysis suggests that further stringency in this respect is unlikely to have a significant effect on attrition in development and that additional work is required to address safety-related failures. Attempts to reduce the number of efficacy- and safety-related failures that may be linked to the physicochemical properties of small-molecule drug candidates have been inconclusive owing to the limited size of data sets from individual companies. Waring and colleagues analyse the largest data set compiled so far on the causes of attrition for oral, small-molecule drug candidates, derived from a pioneering data-sharing effort by AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The pharmaceutical industry remains under huge pressure to address the high attrition rates in drug development. Attempts to reduce the number of efficacy- and safety-related failures by analysing possible links to the physicochemical properties of small-molecule drug candidates have been inconclusive because of the limited size of data sets from individual companies. Here, we describe the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer. The analysis reaffirms that control of physicochemical properties during compound optimization is beneficial in identifying compounds of candidate drug quality and indicates for the first time a link between the physicochemical properties of compounds and clinical failure due to safety issues. The results also suggest that further control of physicochemical properties is unlikely to have a significant effect on attrition rates and that additional work is required to address safety-related failures. Further cross-company collaborations will be crucial to future progress in this area.

High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery.

The cost of drug discovery and development is driven primarily by failure 1 , with only about 10% of clinical programmes eventually receiving approval 2 – 4 . We previously estimated that human genetic evidence doubles the success rate from clinical development to approval 5 . 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. Human genetic evidence increases the success rate of drugs from clinical development to approval but we are still far from reaching peak genetic insights to aid the discovery of targets for more effective drugs.

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments. Recent advances in computational approaches and challenges in their application to streamlining drug discovery are discussed.

Key Points Drug discovery approaches for cancer, as for other therapeutic areas, have typically been divided into two classes: target-based drug discovery (TDD) and phenotypic drug discovery (PDD). Cancer drug discovery poses substantial challenges for both targeted and 'classical' phenotypic drug discovery owing to the number, diversity and plasticity of molecular mechanisms and phenotypes underlying tumour initiation and growth. Discovery origins for all 48 small-molecule cancer drugs approved by the US Food and Drug Administration between 1999 and 2013, and for agents in Phase II and II clinical trials at the end of 2013, were analysed and classified. Although a significant number of approved and investigational cancer drugs could be easily classified as targeted, the majority of which (21 out of 29) are kinase inhibitors, we concluded that very few drugs (four out of 48) were discovered entirely by 'classical' PDD. The remainder were discovered by, or developed from chemical lead matter discovered by a combination of phenotypic and target-based assays. Drug discovery using cytoxicity assays and cancer cell lines, although yielding many of the current standard-of-care chemotherapies, is unlikely to result in further drugs with novel mechanisms of action. Knowledge of the molecular pathways and targets required for specific disease-associated phenotypes, along with the ability to use more disease-relevant cell models, improves the probability of discovering drugs with novel mechanisms of action and clinical efficacy in molecularly defined patient populations. We introduce the concept of 'mechanism-informed phenotypic drug discovery' (MIPDD) to include phenotypic assays for specific molecular pathways and targets. Determining the causal relationships between target inhibition and phenotypic effects may well open up new and unexpected avenues of cancer biology. Such an approach presents the best means of discovering drugs that have both an optimal molecular mechanism of action and a diagnostic hypothesis to enable patient selection leading to clinical responses. There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Moffat and colleagues investigated the contribution of phenotypic assays in oncology by analysing the origins of the new small-molecule cancer drugs approved by the US Food and Drug Administration over the past 15 years. They also discuss technical and biological advances that could empower phenotypic drug discovery in oncology by enabling the development of mechanistically informed phenotypic screens. There has been a resurgence of interest in the use of phenotypic screens in drug discovery as an alternative to target-focused approaches. Given that oncology is currently the most active therapeutic area, and also one in which target-focused approaches have been particularly prominent in the past two decades, we investigated the contribution of phenotypic assays to oncology drug discovery by analysing the origins of all new small-molecule cancer drugs approved by the US Food and Drug Administration (FDA) over the past 15 years and those currently in clinical development. Although the majority of these drugs originated from target-based discovery, we identified a significant number whose discovery depended on phenotypic screening approaches. We postulate that the contribution of phenotypic screening to cancer drug discovery has been hampered by a reliance on 'classical' nonspecific drug effects such as cytotoxicity and mitotic arrest, exacerbated by a paucity of mechanistically defined cellular models for therapeutically translatable cancer phenotypes. However, technical and biological advances that enable such mechanistically informed phenotypic models have the potential to empower phenotypic drug discovery in oncology.

More than a decade after a Nobel Prize was awarded for the discovery of the ubiquitin-proteasome system and clinical approval of proteasome and ubiquitin E3 ligase inhibitors, first-generation deubiquitylating enzyme (DUB) inhibitors are now approaching clinical trials. However, although our knowledge of the physiological and pathophysiological roles of DUBs has evolved tremendously, the clinical development of selective DUB inhibitors has been challenging. In this Review, we discuss these issues and highlight recent advances in our understanding of DUB enzymology and biology as well as technological improvements that have contributed to the current interest in DUBs as therapeutic targets in diseases ranging from oncology to neurodegeneration.