Finding new analgesics: Computational pharmacology faces drug discovery challenges
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Finding new analgesics : Computational pharmacology faces drug discovery challenges. / Barakat, Ahmed; Munro, Gordon; Heegaard, Anne Marie.
In: Biochemical Pharmacology, Vol. 222, 116091, 2024.Research output: Contribution to journal › Review › Research › peer-review
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TY - JOUR
T1 - Finding new analgesics
T2 - Computational pharmacology faces drug discovery challenges
AU - Barakat, Ahmed
AU - Munro, Gordon
AU - Heegaard, Anne Marie
N1 - Publisher Copyright: © 2024 Elsevier Inc.
PY - 2024
Y1 - 2024
N2 - Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.
AB - Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.
KW - Analgesics
KW - Animal behavior
KW - Assays
KW - Computational pharmacology
KW - Data science
KW - Drug discovery
KW - In-silico
KW - Machine learning
KW - Pain
KW - Systems biology
U2 - 10.1016/j.bcp.2024.116091
DO - 10.1016/j.bcp.2024.116091
M3 - Review
C2 - 38412924
AN - SCOPUS:85186737832
VL - 222
JO - Biochemical Pharmacology
JF - Biochemical Pharmacology
SN - 0006-2952
M1 - 116091
ER -
ID: 385503638