Is Target-Based Drug Discovery Efficient? Discovery and “Off-Target” Mechanisms of All Drugs

Journal of Medicinal Chemistry, Journal Year: 2023, Volume and Issue: 66(18), P. 12651 - 12677

Published: Sept. 6, 2023

Target-based drug discovery is the dominant paradigm of discovery; however, a comprehensive evaluation its real-world efficiency lacking. Here, manual systematic review about 32000 articles and patents dating back to 150 years ago demonstrates apparent inefficiency. Analyzing origins all approved drugs reveals that, despite several decades dominance, only 9.4% small-molecule have been discovered through "target-based" assays. Moreover, therapeutic effects even this minimal share cannot be solely attributed reduced their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based may cause productivity crisis in discovery. An evidence-based approach enhance seems prioritizing, selecting optimizing molecules, higher-level observations are closer sought-after using tools like artificial intelligence machine learning.

Language: Английский

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The foundation and architecture of precision medicine in neurology and psychiatry

Trends in Neurosciences, Journal Year: 2023, Volume and Issue: 46(3), P. 176 - 198

Published: Jan. 13, 2023

Neurological and psychiatric diseases have high degrees of genetic pathophysiological heterogeneity, irrespective clinical manifestations. Traditional medical paradigms focused on late-stage syndromic aspects these diseases, with little consideration the underlying biology. Advances in disease modeling methodological design paved way for development precision medicine (PM), an established concept oncology growing attention from other specialties. We propose a PM architecture central nervous system built four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, data science. discuss Alzheimer's (AD), area significant unmet need, as case-in-point proposed framework. AD can be seen one most advanced PM-oriented models compelling catalyzer towards neuroscience drug healthcare practice.

Language: Английский

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Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

Medicinal Research Reviews, Journal Year: 2020, Volume and Issue: 40(6), P. 2386 - 2426

Published: July 13, 2020

Abstract Following two decades of more than 400 clinical trials centered on the “one drug, one target, disease” paradigm, there is still no effective disease‐modifying therapy for Alzheimer's disease (AD). The inherent complexity AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that likely shares common underlying mechanisms intermediate pathophenotypes, or endophenotypes, with other diseases. In review, we consider pathobiology, comorbidity, pleiotropy, therapeutic development, construct relevant endophenotype networks to guide future development. Specifically, discuss six main hypotheses AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular lysosomal dysfunction. We how framework can provide computational experimental strategies drug‐repurposing identification new candidate patients suffering from at risk AD. highlight opportunities endophenotype‐informed, drug discovery AD, by exploiting multi‐omics data. Integration genomics, transcriptomics, radiomics, pharmacogenomics, interactomics (protein–protein interactions) are essential successful discovery. describe technologies including human induced pluripotent stem cells, transgenic mouse/rat models, population‐based retrospective case–control studies be integrated a methodology. summary, endophenotype‐based methodologies will promote development optimize usefulness available data support deep phenotyping patient heterogeneity personalized

Language: Английский

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Finding new and better treatments for psychiatric disorders

Neuropsychopharmacology, Journal Year: 2023, Volume and Issue: 49(1), P. 3 - 9

Published: Aug. 15, 2023

Abstract In contrast to most fields of medicine, progress discover and develop new improved psychiatric drugs has been slow disappointing. The vast majority currently prescribed treat schizophrenia, mood anxiety disorders are arguably no more effective than the first generation introduced well over 50 years ago. With only a few exceptions current work via same fundamental mechanisms action as first-generation agents. Here we describe reasons for this outline number areas research that involve greater reliance on experimental therapeutics utilizing recent advances in neuroscience better understand disease biology. We exemplify potential impact these focus with several examples novel agents have emerged which support our optimism newer, tolerated agents, horizon. Together existing newer could offer markedly functional outcomes millions people still disabled by disorders.

Language: Английский

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Recent applications of quantitative systems pharmacology and machine learning models across diseases

Journal of Pharmacokinetics and Pharmacodynamics, Journal Year: 2021, Volume and Issue: 49(1), P. 19 - 37

Published: Oct. 20, 2021

Language: Английский

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In Vitro to In Vivo Extrapolation Linked to Physiologically Based Pharmacokinetic Models for Assessing the Brain Drug Disposition

The AAPS Journal, Journal Year: 2022, Volume and Issue: 24(1)

Published: Jan. 1, 2022

Abstract Drug development for the central nervous system (CNS) is a complex endeavour with low success rates, as structural complexity of brain and specifically blood-brain barrier (BBB) poses tremendous challenges. Several in vitro systems have been evaluated, but ultimate use these data terms translation to human concentration profiles remains be fully developed. Thus, linking up vitro-to-in vivo extrapolation (IVIVE) strategies physiologically based pharmacokinetic (PBPK) models useful effort that allows better prediction drug concentrations CNS components. Such may overcome some known aspects inter-species differences disposition. Required physiological (i.e. systems) parameters model are derived from quantitative values each organ. However, due inability directly measure humans, compound-specific (drug) often obtained silico or studies. translated through IVIVE which could also applied preclinical observations. In such exercises, limitations assays should adequately understood order verify predictions observed data. This report summarizes state IVIVE-PBPK-linked discusses shortcomings areas further research

Language: Английский

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Reinforcement learning as an innovative model-based approach: Examples from precision dosing, digital health and computational psychiatry

Frontiers in Pharmacology, Journal Year: 2023, Volume and Issue: 13

Published: Feb. 17, 2023

Model-based approaches are instrumental for successful drug development and use. Anchored within pharmacological principles, through mathematical modeling they contribute to the quantification of response variability enables precision dosing. Reinforcement learning (RL)—a set computational methods addressing optimization problems as a continuous process—shows relevance dosing with high flexibility rule adaptation coping dimensional efficacy and/or safety markers, constituting relevant approach take advantage data from digital health technologies. RL can also support contributions applications, recognized key players future healthcare systems, in particular reducing burden non-communicable diseases society. is pivotal psychiatry—a way characterize mental dysfunctions terms aberrant brain computations—and represents an innovative forpsychiatric indications such depression or substance abuse disorders which therapeutics foreseen promising modalities.

Language: Английский

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Analysis of Cerebrospinal Fluid, Plasma β-Amyloid Biomarkers, and Cognition from a 2-Year Phase 2 Trial Evaluating Oral ALZ-801/Valiltramiprosate in APOE4 Carriers with Early Alzheimer’s Disease Using Quantitative Systems Pharmacology Model

Drugs, Journal Year: 2024, Volume and Issue: 84(7), P. 825 - 839

Published: June 20, 2024

ALZ-801/valiltramiprosate is an oral, small-molecule inhibitor of beta-amyloid (Aβ) aggregation and oligomer formation in late-stage development as a disease-modifying therapy for early Alzheimer's disease (AD). The present investigation provides quantitative systems pharmacology (QSP) analysis amyloid fluid biomarkers cognitive results from 2-year ALZ-801 Phase 2 trial APOE4 carriers with AD.

Language: Английский

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Meta-analysis and review of in silico methods in drug discovery – part 1: technological evolution and trends from big data to chemical space

The Pharmacogenomics Journal, Journal Year: 2025, Volume and Issue: 25(3)

Published: April 9, 2025

Language: Английский

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Structural Systems Biology Toolkit (SSBtoolkit): From Molecular Structure to Subcellular Signaling Pathways

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

Here, we introduce the Structural Systems Biology (SSB) toolkit, a Python library that integrates structural macromolecular data with systems biology simulations to model signal-transduction pathways of G-protein-coupled receptors (GPCRs). Our framework streamlines simulation and analysis mathematical models GPCRs cellular pathways, facilitating exploration kinetics induced by ligand-GPCR interactions: dose-response ligand can be modeled, along corresponding change in concentration other signaling molecular species over time, like for instance [Ca2+] or [cAMP]. SSB toolkit brings light possibility easily investigating subcellular effects binding on receptor activation, even presence genetic mutations, thereby enhancing our understanding intricate relationship between ligand-target interactions at level higher-level (patho)physiological response mechanisms.

Language: Английский

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