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Barakat A, Munro G, Heegaard AM. Finding new analgesics: Computational pharmacology faces drug discovery challenges. Biochem Pharmacol 2024; 222:116091. [PMID: 38412924 DOI: 10.1016/j.bcp.2024.116091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/10/2024] [Accepted: 02/23/2024] [Indexed: 02/29/2024]
Abstract
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.
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Affiliation(s)
- Ahmed Barakat
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Pharmacology and Toxicology, Faculty of Pharmacy, Assiut University, Assiut, Egypt.
| | | | - Anne-Marie Heegaard
- Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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Morado J, Mortenson PN, Nissink JWM, Essex JW, Skylaris CK. Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins. J Chem Inf Model 2023; 63:2810-2827. [PMID: 37071825 PMCID: PMC10170518 DOI: 10.1021/acs.jcim.2c01510] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. The results and discussion presented in this study demonstrate that ANI-2x tends to predict stronger-than-expected hydrogen bonding and overstabilize global minima and shows problems related to inadequate description of dispersion interactions. Furthermore, while ANI-2x is a viable model for modeling in the gas phase, conventional force fields still play an important role, especially for condensed-phase simulations. Overall, this study highlights the strengths and weaknesses of each model, providing guidelines for the use and future development of force fields and machine learning potentials.
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Affiliation(s)
- João Morado
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Paul N Mortenson
- Astex Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge CB4 0QA, United Kingdom
| | - J Willem M Nissink
- Computational Chemistry, Oncology R&D, AstraZeneca, Cambridge CB4 0WG, United Kingdom
| | - Jonathan W Essex
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
| | - Chris-Kriton Skylaris
- School of Chemistry, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom
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Malkusch S, Hahnefeld L, Gurke R, Lötsch J. Visually guided preprocessing of bioanalytical laboratory data using an interactive R notebook (pguIMP). CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:1371-1381. [PMID: 34598320 PMCID: PMC8592507 DOI: 10.1002/psp4.12704] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 07/06/2021] [Accepted: 08/10/2021] [Indexed: 01/05/2023]
Abstract
The evaluation of pharmacological data using machine learning requires high data quality. Therefore, data preprocessing, that is, cleaning analytical laboratory errors, replacing missing values or outliers, and transforming data adequately before actual data analysis, is crucial. Because current tools available for this purpose often require programming skills, preprocessing tools with graphical user interfaces that can be used interactively are needed. In collaboration between data scientists and experts in bioanalytical diagnostics, a graphical software package for data preprocessing called pguIMP is proposed, which contains a fixed sequence of preprocessing steps to enable reproducible interactive data preprocessing. As an R-based package, it also allows direct integration into this data science environment without requiring any programming knowledge. The implementation of contemporary data processing methods, including machine-learning-based imputation techniques, ensures the generation of corrected and cleaned bioanalytical data sets that preserve data structures such as clusters better than is possible with classical methods. This was evaluated on bioanalytical data sets from lipidomics and drug research using k-nearest-neighbors-based imputation followed by k-means clustering and density-based spatial clustering of applications with noise. The R package provides a Shiny-based web interface designed to be easy to use for non-data analysis experts. It is demonstrated that the spectrum of methods provided is suitable as a standard pipeline for preprocessing bioanalytical data in biomedical research domains. The R package pguIMP is freely available at the comprehensive R archive network (https://cran.r-project.org/web/packages/pguIMP/index.html).
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Affiliation(s)
- Sebastian Malkusch
- Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany
| | - Lisa Hahnefeld
- Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany
| | - Robert Gurke
- Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
| | - Jörn Lötsch
- Institute of Clinical Pharmacology, Goethe-University, Frankfurt am Main, Germany.,Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
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Pricing through health apps generated data-Digital dividend as a game changer: Discrete choice experiment. PLoS One 2021; 16:e0254786. [PMID: 34310618 PMCID: PMC8312968 DOI: 10.1371/journal.pone.0254786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Accepted: 07/02/2021] [Indexed: 11/20/2022] Open
Abstract
Objectives The objective of this paper is to study under which circumstances wearable and health app users would accept a compensation payment, namely a digital dividend, to share their self-tracked health data. Methods We conducted a discrete choice experiment alternative, a separated adaptive dual response. We chose this approach to reduce extreme response behavior, considering the emotionally-charged topic of health data sales, and to measure willingness to accept. Previous experiments in lab settings led to demands for high monetary compensation. After a first online survey and two pre-studies, we validated four attributes for the final online study: monthly bonus payment, stakeholder handling the data (e.g., health insurer, pharmaceutical or medical device companies, universities), type of data, and data sales to third parties. We used a random utility framework to evaluate individual choice preferences. To test the expected prices of the main study for robustness, we assigned respondents randomly to one of two identical questionnaires with varying price ranges. Results Over a period of three weeks, 842 respondents participated in the main survey, and 272 respondents participated in the second survey. The participants considered transparency about data processing and no further data sales to third parties as very important to the decision to share data with different stakeholders, as well as adequate monetary compensation. Price expectations resulting from the experiment were high; pharmaceutical and medical device companies would have to pay an average digital dividend of 237.30€/month for patient generated health data of all types. We also observed an anchor effect, which means that people formed price expectations during the process and not ex ante. We found a bimodal distribution between relatively low price expectations and relatively high price expectations, which shows that personal data selling is a divisive societal issue. However, the results indicate that a digital dividend could be an accepted economic incentive system to gather large-scale, self-tracked data for research and development purposes. After the COVID-19 crisis, price expectations might change due to public sensitization to the need for big data research on patient generated health data. Conclusion A continuing success of existing data donation models is highly unlikely. The health care sector needs to develop transparency and trust in data processing. An adequate digital dividend could be an effective long-term measure to convince a diverse and large group of people to share high-quality, continuous data for research purposes.
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Sahneh F, Balk MA, Kisley M, Chan CK, Fox M, Nord B, Lyons E, Swetnam T, Huppenkothen D, Sutherland W, Walls RL, Quinn DP, Tarin T, LeBauer D, Ribes D, Birnie DP, Lushbough C, Carr E, Nearing G, Fischer J, Tyle K, Carrasco L, Lang M, Rose PW, Rushforth RR, Roy S, Matheson T, Lee T, Brown CT, Teal TK, Papeș M, Kobourov S, Merchant N. Ten simple rules to cultivate transdisciplinary collaboration in data science. PLoS Comput Biol 2021; 17:e1008879. [PMID: 33983959 PMCID: PMC8118297 DOI: 10.1371/journal.pcbi.1008879] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Affiliation(s)
- Faryad Sahneh
- Data Science Institute, University of Arizona, Tucson, Arizona, United States of America
- Computer Science Department, University of Arizona, Tucson, Arizona, United States of America
- * E-mail:
| | - Meghan A. Balk
- BIO5 Institute, University of Arizona, Tucson, Arizona, United States of America
- National Museum of Natural History, Department of Paleontology, Washington, District of Columbia, United States of America
| | - Marina Kisley
- Computer Science Department, University of Arizona, Tucson, Arizona, United States of America
| | - Chi-kwan Chan
- Data Science Institute, University of Arizona, Tucson, Arizona, United States of America
- Steward Observatory and Department of Astronomy, University of Arizona, Tucson, Arizona, United States of America
| | - Mercury Fox
- Data Science Institute, University of Arizona, Tucson, Arizona, United States of America
- CODATA Center of Excellence in Data for Society, Washington, District of Columbia, United States of America
- School of Information, University of Arizona, Tucson, Arizona, United States of America
- Native Nations Institute, University of Arizona, Tucson, Arizona, United States of America
- Center for Digital Society and Data Studies, University of Arizona, Tucson, Arizona, United States of America
| | - Brian Nord
- Fermi National Accelerator Laboratory, Batavia, Illinois, United States of America
- Kavli Institute for Cosmological Physics, University of Chicago, Chicago, Illinois, United States of America
- Department of Astronomy and Astrophysics, University of Chicago, Illinois, United States of America
| | - Eric Lyons
- BIO5 Institute, University of Arizona, Tucson, Arizona, United States of America
- School of Plant Sciences, University of Arizona, Tucson, Arizona, United States of America
- CyVerse, University of Arizona, Tucson, Arizona, United States of America
| | - Tyson Swetnam
- BIO5 Institute, University of Arizona, Tucson, Arizona, United States of America
| | - Daniela Huppenkothen
- DIRAC Institute, Department of Astronomy, University of Washington, Seattle, Washington, United States of America
- eScience Institute, University of Washington, Seattle, Washington, United States of America
| | - Will Sutherland
- Department of Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States of America
| | - Ramona L. Walls
- BIO5 Institute, University of Arizona, Tucson, Arizona, United States of America
| | - Daven P. Quinn
- Department of Geoscience, University of Wisconsin–Madison, Madison, Wisconsin, United States of America
| | - Tonantzin Tarin
- Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City, Mexico
| | - David LeBauer
- College of Agriculture and Life Sciences, University of Arizona, Tucson, Arizona, United States of America
| | - David Ribes
- Department of Human Centered Design and Engineering, University of Washington, Seattle, Washington, United States of America
| | - Dunbar P. Birnie
- Department of Materials Science and Engineering, Rutgers University, Piscataway, New Jersey, United States of America
| | - Carol Lushbough
- Biomedical Engineering Department, University of South Dakota, Sioux Falls, South Dakota, United States of America
- BioSNTR, Brookings, South Dakota, United States of America
| | - Eric Carr
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Grey Nearing
- Google Research, Mountain View, California, United States of America
| | - Jeremy Fischer
- Pervasive Technology Institute, Indiana University Bloomington, Bloomington, Indiana, United States of America
- JetStream Cloud, Indiana University Bloomington, Bloomington, Indiana, United States of America
| | - Kevin Tyle
- Atmospheric & Environmental Sciences, University at Albany, Albany, New York, United States of America
| | - Luis Carrasco
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Meagan Lang
- National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Peter W. Rose
- San Diego Supercomputer Center, University of California, San Diego, La Jolla, California, United States of America
| | - Richard R. Rushforth
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona, United States of America
| | - Samapriya Roy
- Planet Labs, San Francisco, California, United States of America
| | - Thomas Matheson
- NSF’s National Optical-Infrared Astronomy Research Laboratory, Tucson, Arizona, United States of America
| | - Tina Lee
- CyVerse, University of Arizona, Tucson, Arizona, United States of America
| | - C. Titus Brown
- Department of Population Health and Reproduction, University of California, Davis, Davis, California, United States of America
| | - Tracy K. Teal
- Dryad, Durham, North Carolina, United States of America
| | - Monica Papeș
- National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennessee, United States of America
- Ecology & Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, United States of America
| | - Stephen Kobourov
- Computer Science Department, University of Arizona, Tucson, Arizona, United States of America
| | - Nirav Merchant
- Data Science Institute, University of Arizona, Tucson, Arizona, United States of America
- CyVerse, University of Arizona, Tucson, Arizona, United States of America
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