Predictive validity in drug discovery: what it is, why it matters and how to improve it

Scannell, Jack W.; Bosley, James R.; Hickman, John A.; Dawson, Gerard R.; Truebel, Hubert; Ferreira, Guilherme S.; Richards, Duncan; Treherne, J. Mark

doi:10.1038/s41573-022-00552-x

Cited by 41 publications

(33 citation statements)

References 267 publications

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“…Antimicrobial resistance in animal and human pathogens represents a major global health crisis since the prevalence of multidrug-resistant (MDR) clinical bacterial isolates are currently on the rise [ 1 , 2 ]. The World Health Organization’s priority list of resistant bacteria includes three Gram-negative species at the critical ranking, the highest level of concern [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Phentolamine Significantly Enhances Macrolide Antibiotic Antibacterial Activity against MDR Gram-Negative Bacteria

Cui

Zheng

et al. 2023

Antibiotics

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Objectives: Multidrug-resistant (MDR) Gram-negative bacterial infections have limited treatment options due to the impermeability of the outer membrane. New therapeutic strategies or agents are urgently needed, and combination therapies using existing antibiotics are a potentially effective means to treat these infections. In this study, we examined whether phentolamine can enhance the antibacterial activity of macrolide antibiotics against Gram-negative bacteria and investigated its mechanism of action. Methods: Synergistic effects between phentolamine and macrolide antibiotics were evaluated by checkerboard and time–kill assays and in vivo using a Galleria mellonella infection model. We utilized a combination of biochemical tests (outer membrane permeability, ATP synthesis, ΔpH gradient measurements, and EtBr accumulation assays) with scanning electron microscopy to clarify the mechanism of phentolamine enhancement of macrolide antibacterial activity against Escherichia coli. Results: In vitro tests of phentolamine combined with the macrolide antibiotics erythromycin, clarithromycin, and azithromycin indicated a synergistic action against E. coli test strains. The fractional concentration inhibitory indices (FICI) of 0.375 and 0.5 indicated a synergic effect that was consistent with kinetic time–kill assays. This synergy was also seen for Salmonella typhimurium, Klebsiella pneumoniae, and Actinobacter baumannii but not Pseudomonas aeruginosa. Similarly, a phentolamine/erythromycin combination displayed significant synergistic effects in vivo in the G. mellonella model. Phentolamine added singly to bacterial cells also resulted in direct outer membrane damage and was able to dissipate and uncouple membrane proton motive force from ATP synthesis that, resulted in enhanced cytoplasmic antibiotic accumulation via reduced efflux pump activity. Conclusions: Phentolamine potentiates macrolide antibiotic activity via reducing efflux pump activity and direct damage to the outer membrane leaflet of Gram-negative bacteria both in vitro and in vivo.

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Section: Introductionmentioning

confidence: 99%

Phentolamine Significantly Enhances Macrolide Antibiotic Antibacterial Activity against MDR Gram-Negative Bacteria

Cui

Zheng

et al. 2023

Antibiotics

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“…Drug discovery is expensive. Considering a representative target portfolio, high-throughput screening (HTS) is presently the most widely applicable technology for delivering chemical entry points for drug discovery campaigns ( Scannell et al, 2022 ), but despite its popularity, this high-cost method can result in low hit rates ( Zeng et al, 2020 ). The attrition rates of identified hits are further increased during the validation phase and optimization stage due to inherent deficits in the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties ( Feinberg et al, 2020 ; Xiong et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification

Bian

Kwon

Liu

et al. 2023

Front. Mol. Biosci.

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High-throughput screening (HTS) methods enable the empirical evaluation of a large scale of compounds and can be augmented by virtual screening (VS) techniques to save time and money by using potential active compounds for experimental testing. Structure-based and ligand-based virtual screening approaches have been extensively studied and applied in drug discovery practice with proven outcomes in advancing candidate molecules. However, the experimental data required for VS are expensive, and hit identification in an effective and efficient manner is particularly challenging during early-stage drug discovery for novel protein targets. Herein, we present our TArget-driven Machine learning-Enabled VS (TAME-VS) platform, which leverages existing chemical databases of bioactive molecules to modularly facilitate hit finding. Our methodology enables bespoke hit identification campaigns through a user-defined protein target. The input target ID is used to perform a homology-based target expansion, followed by compound retrieval from a large compilation of molecules with experimentally validated activity. Compounds are subsequently vectorized and adopted for machine learning (ML) model training. These machine learning models are deployed to perform model-based inferential virtual screening, and compounds are nominated based on predicted activity. Our platform was retrospectively validated across ten diverse protein targets and demonstrated clear predictive power. The implemented methodology provides a flexible and efficient approach that is accessible to a wide range of users. The TAME-VS platform is publicly available at https://github.com/bymgood/Target-driven-ML-enabled-VS to facilitate early-stage hit identification.

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“…The modeling of healthy and diseased function via networks is extremely popular today, leading to the near-universal assumption among both academia and the biotech industry that all control must be exerted at the hardware (molecular medicine) level. Despite some successes, it is widely acknowledged that despite the ever-increasing deluge of omics data and a mature set of computational tools for understanding dynamical systems, there is immense unmet medical need [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Cellular Signaling Pathways as Plastic, Proto-cognitive Systems: Implications for Biomedicine

Mathews¹,

Chang²,

Devlin³

et al. 2022

Preprint

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Many aspects of health and disease are modeled using the abstraction of a “pathway” – a set of protein or other subcellular activities with specified functional linkages between them. This metaphor is a paradigmatic case of a deterministic, mechanistic framework that focuses biomedical intervention strategies on altering the members of this network or the up/down-regulation links between them – rewiring the molecular hardware. However, protein pathways and transcriptional networks share important properties with neural networks and related dynamical systems, which implies that they can exhibit interesting and unexpected capabilities such as trainability (memory) and information processing in a context-sensitive manner. Specifically, they may be amenable to manipulation via their history of stimuli (equivalent to experiences, in behavioral science). If true, this would enable a new class of biomedical interventions that targets aspects of the dynamic physiological “software” implemented by pathways and gene-regulatory networks. Here, we propose an expanded view of pathways from the perspective of basal cognition. We review clinical and laboratory data supporting the idea that a broader understanding of pathways (and how they process contextual information across scales) is a limiting factor for progress in many areas of physiology and neurobiology. We argue that a fuller understanding of the functionality and tractability of pathways must go beyond a focus on the mechanistic details of their structure, to encompass their physiological history and embedding within higher levels of organization in the organism, with numerous implications for data science addressing health and disease. Exploiting tools and concepts from behavioral and cognitive sciences is not just a philosophical stance on biochemical processes; at stake is a new roadmap for overcoming the limitations of today’s pharmacological strategies and for the inference of future therapeutic interventions impacting a wide range of disease states.

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Predictive validity in drug discovery: what it is, why it matters and how to improve it

Cited by 41 publications

References 267 publications

Phentolamine Significantly Enhances Macrolide Antibiotic Antibacterial Activity against MDR Gram-Negative Bacteria

Phentolamine Significantly Enhances Macrolide Antibiotic Antibacterial Activity against MDR Gram-Negative Bacteria

Target-driven machine learning-enabled virtual screening (TAME-VS) platform for early-stage hit identification

Cellular Signaling Pathways as Plastic, Proto-cognitive Systems: Implications for Biomedicine

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