1
|
Kırboğa KK, Abbasi S, Küçüksille EU. Explainability and white box in drug discovery. Chem Biol Drug Des 2023; 102:217-233. [PMID: 37105727 DOI: 10.1111/cbdd.14262] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 03/24/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023]
Abstract
Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed.
Collapse
Affiliation(s)
- Kevser Kübra Kırboğa
- Bioengineering Department, Bilecik Seyh Edebali University, Bilecik, Turkey
- Informatics Institute, Istanbul Technical University, Maslak, Turkey
| | - Sumra Abbasi
- Department of Biological Sciences, National of Medical Sciences, Rawalpindi, Pakistan
| | - Ecir Uğur Küçüksille
- Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey
| |
Collapse
|
2
|
Thomas SP, Denizer E, Zuffa S, Best BM, Bode L, Chambers CD, Dorrestein PC, Liu GY, Momper JD, Nizet V, Tsunoda SM, Tremoulet AH. Transfer of antibiotics and their metabolites in human milk: Implications for infant health and microbiota. Pharmacotherapy 2023; 43:442-451. [PMID: 36181712 PMCID: PMC10763576 DOI: 10.1002/phar.2732] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/21/2022] [Accepted: 09/22/2022] [Indexed: 05/17/2023]
Abstract
Antibiotics are an essential tool for perinatal care. While antibiotics can play a life-saving role for both parents and infants, they also cause collateral damage to the beneficial bacteria that make up the host gut microbiota. This is especially true for infants, whose developing gut microbiota is uniquely sensitive to antibiotic perturbation. Emerging evidence suggests that disruption of these bacterial populations during this crucial developmental window can have long-term effects on infant health and development. Although most current studies have focused on microbial disruptions caused by direct antibiotic administration to infants or prenatal exposure to antibiotics administered to the mother, little is known about whether antibiotics in human milk may pose similar risks to the infant. This review surveys current data on antibiotic transfer during lactation and highlights new methodologies to assess drug transfer in human milk. Finally, we provide recommendations for future work to ensure antibiotic use in lactating parents is safe and effective for both parents and infants.
Collapse
Affiliation(s)
- Sydney P. Thomas
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA
- Collaborative Mass Spectrometry Innovation Center, UC San Diego, La Jolla, California, USA
| | - Erce Denizer
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA
- Collaborative Mass Spectrometry Innovation Center, UC San Diego, La Jolla, California, USA
| | - Simone Zuffa
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA
- Collaborative Mass Spectrometry Innovation Center, UC San Diego, La Jolla, California, USA
| | - Brookie M. Best
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA
- Pediatrics Department-Rady Children's Hospital San Diego, UC San Diego School of Medicine, La Jolla, California, USA
| | - Lars Bode
- Pediatrics Department-Rady Children's Hospital San Diego, UC San Diego School of Medicine, La Jolla, California, USA
- Mother-Milk-Infant Center of Research Excellence (MOMI CORE), UC San Diego, La Jolla, California, USA
| | - Christina D. Chambers
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA
- Pediatrics Department-Rady Children's Hospital San Diego, UC San Diego School of Medicine, La Jolla, California, USA
- Hebert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, California, USA
| | - Pieter C. Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA
- Collaborative Mass Spectrometry Innovation Center, UC San Diego, La Jolla, California, USA
| | - George Y. Liu
- Pediatrics Department-Rady Children's Hospital San Diego, UC San Diego School of Medicine, La Jolla, California, USA
| | - Jeremiah D. Momper
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA
| | - Victor Nizet
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA
- Pediatrics Department-Rady Children's Hospital San Diego, UC San Diego School of Medicine, La Jolla, California, USA
| | - Shirley M. Tsunoda
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, USA
| | - Adriana H. Tremoulet
- Pediatrics Department-Rady Children's Hospital San Diego, UC San Diego School of Medicine, La Jolla, California, USA
| |
Collapse
|
3
|
Min J, Tu J, Xu C, Lukas H, Shin S, Yang Y, Solomon SA, Mukasa D, Gao W. Skin-Interfaced Wearable Sweat Sensors for Precision Medicine. Chem Rev 2023; 123:5049-5138. [PMID: 36971504 PMCID: PMC10406569 DOI: 10.1021/acs.chemrev.2c00823] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines state-of-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.
Collapse
Affiliation(s)
- Jihong Min
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Jiaobing Tu
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Changhao Xu
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Heather Lukas
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Soyoung Shin
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Yiran Yang
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Samuel A. Solomon
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Daniel Mukasa
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| | - Wei Gao
- Andrew and Peggy Cherng Department of Medical Engineering, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, California, 91125, USA
| |
Collapse
|
4
|
Waters LJ, Quah XL. Predicting skin permeability using HuskinDB. Sci Data 2022; 9:584. [PMID: 36151144 PMCID: PMC9508232 DOI: 10.1038/s41597-022-01698-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 09/14/2022] [Indexed: 11/16/2022] Open
Abstract
A freely accessible database has recently been released that provides measurements available in the literature on human skin permeation data, known as the ‘Human Skin Database – HuskinDB’. Although this database is extremely useful for sourcing permeation data to help with toxicity and efficacy determination, it cannot be beneficial when wishing to consider unlisted, or novel compounds. This study undertakes analysis of the data from within HuskinDB to create a model that predicts permeation for any compound (within the range of properties used to create the model). Using permeability coefficient (Kp) data from within this resource, several models were established for Kp values for compounds of interest by varying the experimental parameters chosen and using standard physicochemical data. Multiple regression analysis facilitated creation of one particularly successful model to predict Kp through human skin based only on three chemical properties. The model transforms the dataset from simply a resource of information to a more beneficial model that can be used to replace permeation testing for a wide range of compounds.
Collapse
Affiliation(s)
- Laura J Waters
- School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK.
| | - Xin Ling Quah
- School of Applied Sciences, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH, UK
| |
Collapse
|
5
|
An Overview of the Latest Metabolomics Studies on Atopic Eczema with New Directions for Study. Int J Mol Sci 2022; 23:ijms23158791. [PMID: 35955924 PMCID: PMC9368995 DOI: 10.3390/ijms23158791] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/01/2022] [Accepted: 08/03/2022] [Indexed: 12/21/2022] Open
Abstract
Atopic eczema (AE) is an inflammatory skin disorder affecting approximately 20% of children worldwide and early onset can lead to asthma and allergies. Currently, the mechanisms of the disease are not fully understood. Metabolomics, the analysis of small molecules in the skin produced by the host and microbes, opens a window to observe the mechanisms of the disease which then may lead to new drug targets for AE treatment. Here, we review the latest advances in AE metabolomics, highlighting both the lipid and non-lipid molecules, along with reviewing the metabolites currently known to reside in the skin.
Collapse
|
6
|
Panitchpakdi M, Weldon KC, Jarmusch AK, Gentry EC, Choi A, Sepulveda Y, Aguirre S, Sun K, Momper JD, Dorrestein PC, Tsunoda SM. Non-invasive skin sampling detects systemically administered drugs in humans. PLoS One 2022; 17:e0271794. [PMID: 35881585 PMCID: PMC9321436 DOI: 10.1371/journal.pone.0271794] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 07/07/2022] [Indexed: 01/26/2023] Open
Abstract
Clinical testing typically relies on invasive blood draws and biopsies. Alternative methods of sample collection are continually being developed to improve patient experience; swabbing the skin is one of the least invasive sampling methods possible. To show that skin swabs in combination with untargeted mass spectrometry (metabolomics) can be used for non-invasive monitoring of an oral drug, we report the kinetics and metabolism of diphenhydramine in healthy volunteers (n = 10) over the course of 24 hours in blood and three regions of the skin. Diphenhydramine and its metabolites were observed on the skin after peak plasma levels, varying by compound and skin location, and is an illustrative example of how systemically administered molecules can be detected on the skin surface. The observation of diphenhydramine directly from the skin supports the hypothesis that both parent drug and metabolites can be qualitatively measured from a simple non-invasive swab of the skin surface. The mechanism of the drug and metabolites pathway to the skin’s surface remains unknown.
Collapse
Affiliation(s)
- Morgan Panitchpakdi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
- Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, United States of America
| | - Kelly C. Weldon
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
- Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, United States of America
- Center for Microbiome Innovation, University of California, San Diego, La Jolla, California, United States of America
| | - Alan K. Jarmusch
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
- Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, United States of America
- Immunity, Inflammation, and Disease Laboratory, Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, United States of America
| | - Emily C. Gentry
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
- Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, United States of America
| | - Arianna Choi
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
| | - Yadira Sepulveda
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
| | - Shaden Aguirre
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
- Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, United States of America
| | - Kunyang Sun
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
- Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, United States of America
| | - Jeremiah D. Momper
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
| | - Pieter C. Dorrestein
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
- Collaborative Mass Spectrometry Innovation Center, University of California, San Diego, La Jolla, California, United States of America
- Department of Pediatrics, School of Medicine, University of California, San Diego, La Jolla, California, United States of America
| | - Shirley M. Tsunoda
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California, United States of America
- * E-mail:
| |
Collapse
|