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On average, an approved drug currently costs US$2–3 billion and takes more than 10 years to develop 1 . In part, this is due to expensive and time-consuming wet-laboratory experiments, poor initial hit compounds and the high attrition rates in the (pre-)clinical phases. Structure-based virtual screening has the potential to mitigate these problems. With structure-based virtual screening, the quality of the hits improves with the number of compounds screened 2 . However, despite the fact that large databases of compounds exist, the ability to carry out large-scale structure-based virtual screening on computer clusters in an accessible, efficient and flexible manner has remained difficult. Here we describe VirtualFlow, a highly automated and versatile open-source platform with perfect scaling behaviour that is able to prepare and efficiently screen ultra-large libraries of compounds. VirtualFlow is able to use a variety of the most powerful docking programs. Using VirtualFlow, we prepared one of the largest and freely available ready-to-dock ligand libraries, with more than 1.4 billion commercially available molecules. To demonstrate the power of VirtualFlow, we screened more than 1 billion compounds and identified a set of structurally diverse molecules that bind to KEAP1 with submicromolar affinity. One of the lead inhibitors (iKeap1) engages KEAP1 with nanomolar affinity (dissociation constant ( K d ) = 114 nM) and disrupts the interaction between KEAP1 and the transcription factor NRF2. This illustrates the potential of VirtualFlow to access vast regions of the chemical space and identify molecules that bind with high affinity to target proteins. VirtualFlow, an open-source drug discovery platform, enables the efficient preparation and virtual screening of ultra-large ligand libraries to identify molecules that bind with high affinity to target proteins.

Naivety about promiscuous, assay-duping molecules is polluting the literature and wasting resources, warn Jonathan Baell and Michael A. Walters.

While the COVID-19 pandemic has spurred intense research and collaborative discovery worldwide, the development of a safe, effective, and targeted antiviral from the ground up is time intensive. Therefore, most antiviral discovery efforts are focused on the re-purposing of clinical stage or approved drugs. While emerging data on drugs undergoing COVID-19 repurpose are intriguing, there is an undeniable need to develop broad-spectrum antivirals to prevent future viral pandemics of unknown origin. The ideal drug to curtail rapid viral spread would be a broad-acting agent with activity against a wide range of viruses. Such a drug would work by modulating host-proteins that are often shared by multiple virus families thereby enabling preemptive drug development and therefore rapid deployment at the onset of an outbreak. Targeting host-pathways and cellular proteins that are hijacked by viruses can potentially offer broad-spectrum targets for the development of future antiviral drugs. Such host-directed antivirals are also likely to offer a higher barrier to the development and selection of drug resistant mutations. Given that most approved antivirals do not target host-proteins, we reinforce the need for the development of such antivirals that can be used in pre- and post-exposure populations.

Key Points Existing preclinical models have a limited ability to test 'therapeutic hypotheses'; that is, whether perturbing a target in a given manner would benefit patients and have minimal toxicity. 'Experiments of nature', including human genetics, provide an estimate of dose–response curves at the time of target validation. There is an increasing number of studies in the literature demonstrating that genes with a series of disease-associated alleles represent promising drug targets. Here, we provide objective criteria to help prioritize research on the most promising targets and ultimately nominate a gene product as the target for a drug development programme. We highlight important limitations of human genetics in target validation, including a commentary on the genetic architecture of common diseases. We also discuss the role of genome-wide association studies (GWASs) and large-scale sequencing projects in drug discovery, emphasizing the importance of precompetitive collaborations that make clinical and genetic data available in a responsible manner. Many clinical trial failures can be traced back to the limited predictive value of preclinical models of disease. Plenge and colleagues discuss how knowledge from human genetics, such as naturally occurring mutations in humans that affect the activity of particular proteins, can be used as a tool to more effectively prioritize molecular targets in drug development. More than 90% of the compounds that enter clinical trials fail to demonstrate sufficient safety and efficacy to gain regulatory approval. Most of this failure is due to the limited predictive value of preclinical models of disease, and our continued ignorance regarding the consequences of perturbing specific targets over long periods of time in humans. 'Experiments of nature' — naturally occurring mutations in humans that affect the activity of a particular protein target or targets — can be used to estimate the probable efficacy and toxicity of a drug targeting such proteins, as well as to establish causal rather than reactive relationships between targets and outcomes. Here, we describe the concept of dose–response curves derived from experiments of nature, with an emphasis on human genetics as a valuable tool to prioritize molecular targets in drug development. We discuss empirical examples of drug–gene pairs that support the role of human genetics in testing therapeutic hypotheses at the stage of target validation, provide objective criteria to prioritize genetic findings for future drug discovery efforts and highlight the limitations of a target validation approach that is anchored in human genetics.

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies. The lack of a unifying metric characterizing combinatorial drug interactions has impeded the development of combinatorial therapies. Here, the authors present MuSyC, a consensus synergy metric that overcomes several caveats associated with other, popular metrics.

Key Points Academic drug discovery offers an opportunity to effectively harness curiosity-driven research to improve human and animal health, but it is not without risk. We believe that the associated risks can be managed by considering at least five factors that affect the success or failure of projects: organization, target selection, assay design, medicinal chemistry and preclinical pharmacology. This manuscript presents guidelines for reducing the risk that can be caused by poor planning in any of these areas. The recent growth in the number of academic drug discovery centres is providing new opportunities to couple the curiosity-driven research culture in academia with rigorous preclinical drug discovery practices used in industry. To realize the potential of these opportunities, it is important that academic researchers understand the risks in several key areas — including organization, target selection, assay design, medicinal chemistry and preclinical pharmacology — which are discussed in this article. The number of academic drug discovery centres has grown considerably in recent years, providing new opportunities to couple the curiosity-driven research culture in academia with rigorous preclinical drug discovery practices used in industry. To fully realize the potential of these opportunities, it is important that academic researchers understand the risks inherent in preclinical drug discovery, and that translational research programmes are effectively organized and supported at an institutional level. In this article, we discuss strategies to mitigate risks in several key aspects of preclinical drug discovery at academic drug discovery centres, including organization, target selection, assay design, medicinal chemistry and preclinical pharmacology.

Society faces a crisis of rising antibiotic resistance even as the pipeline of new antibiotics has been drying up. Antibiotics are a public trust; every individual's use of antibiotics affects their efficacy for everyone else. As such, responses to the antibiotic crisis must take a societal perspective. The market failure of antibiotics is due to a combination of scientific challenges to discovering and developing new antibiotics, unfavorable economics, and a hostile regulatory environment. Scientific solutions include changing the way we screen for new antibiotics. More transformationally, developing new treatments that seek to disarm pathogens without killing them, or that modulate the host inflammatory response to infection, will reduce selective pressure and hence minimize resistance emergence. Economic transformation will require new business models to support antibiotic development. Finally, regulatory reform is needed so that clinical development programs are feasible, rigorous, and clinically relevant. Pulmonary and critical care specialists can have tremendous impact on the continued availability of effective antibiotics. Encouraging use of molecular diagnostic tests to allow pathogen-targeted, narrow-spectrum antibiotic therapy, using short rather than unnecessarily long course therapy, reducing inappropriate antibiotic use for probable viral infections, and reducing infection rates will help preserve the antibiotics we have for future generations.

Our ability to evaluate outcomes which genuinely reflect patients’ unmet needs, hopes and concerns is of pivotal importance. However, much current clinical research and practice falls short of this objective by selecting outcome measures which do not capture patient value to the fullest. In this Opinion, we discuss Patient-Centered Outcomes Measures (PCOMs), which have the potential to systematically incorporate patient perspectives to measure those outcomes that matter most to patients. We argue for greater multi-stakeholder collaboration to develop PCOMs, with rare disease patients and families at the center. Beyond advancing the science of patient input, PCOMs are powerful tools to translate care or observed treatment benefit into an ‘interpretable’ measure of patient benefit, and thereby help demonstrate clinical effectiveness. We propose mixed methods psychometric research as the best route to deliver fit-for-purpose PCOMs in rare diseases, as this methodology brings together qualitative and quantitative research methods in tandem with the explicit aim to efficiently utilise data from small samples. And, whether one opts to develop a brand-new PCOM or to select or adapt an existing outcome measure for use in a rare disease, the anchors remain the same: patients, their daily experience of the rare disease, their preferences, core concepts and values. Ultimately, existing value frameworks, registries, and outcomes-based contracts largely fall short of consistently measuring the full range of outcomes that matter to patients. We argue that greater use of PCOMs in rare diseases would enable a fast track to Patient-Centered Care.

Within the European Immunogenicity Platform (EIP) (http://www.e-i-p.eu), the Protein Characterization Subcommittee (EIP-PCS) has been established to discuss and exchange experience of protein characterization in relation to unwanted immunogenicity. In this commentary, we, as representatives of EIP-PCS, review the current state of methods for analysis of protein aggregates. Moreover, we elaborate on why these methods should be used during product development and make recommendations to the biotech community with regard to strategies for their application during the development of protein therapeutics.

In recent years, even with remarkable scientific advancements and a significant increase of global research and development spending, drugs are frequently withdrawn from markets. This is primarily due to their side effects or toxicities. Drug molecules often interact with multiple targets, coined as polypharmacology, and the unintended drug-target interactions could cause side effects. Polypharmacology remains one of the major challenges in drug development, and it opens novel avenues to rationally design the next generation of more effective, but less toxic, therapeutic agents. This review outlines the latest progress and challenges in polypharmacology studies.