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Janssen A, Bennis FC, Mathôt RAA. Adoption of Machine Learning in Pharmacometrics: An Overview of Recent Implementations and Their Considerations. Pharmaceutics 2022; 14:pharmaceutics14091814. [PMID: 36145562 PMCID: PMC9502080 DOI: 10.3390/pharmaceutics14091814] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 08/17/2022] [Accepted: 08/22/2022] [Indexed: 11/23/2022] Open
Abstract
Pharmacometrics is a multidisciplinary field utilizing mathematical models of physiology, pharmacology, and disease to describe and quantify the interactions between medication and patient. As these models become more and more advanced, the need for advanced data analysis tools grows. Recently, there has been much interest in the adoption of machine learning (ML) algorithms. These algorithms offer strong function approximation capabilities and might reduce the time spent on model development. However, ML tools are not yet an integral part of the pharmacometrics workflow. The goal of this work is to discuss how ML algorithms have been applied in four stages of the pharmacometrics pipeline: data preparation, hypothesis generation, predictive modelling, and model validation. We will also discuss considerations before the use of ML algorithms with respect to each topic. We conclude by summarizing applications that hold potential for adoption by pharmacometricians.
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Affiliation(s)
- Alexander Janssen
- Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam University Medical Center, 1105 Amsterdam, The Netherlands
- Correspondence:
| | - Frank C. Bennis
- Quantitative Data Analytics Group, Department of Computer Science, Vrije Universiteit Amsterdam, 1081 Amsterdam, The Netherlands
| | - Ron A. A. Mathôt
- Department of Clinical Pharmacology, Hospital Pharmacy, Amsterdam University Medical Center, 1105 Amsterdam, The Netherlands
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De Pretis F, Landes J, Peden W. Artificial intelligence methods for a Bayesian epistemology-powered evidence evaluation. J Eval Clin Pract 2021; 27:504-512. [PMID: 33569874 DOI: 10.1111/jep.13542] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 12/09/2020] [Accepted: 01/01/2021] [Indexed: 12/31/2022]
Abstract
RATIONALE, AIMS AND OBJECTIVES The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support, and learning probabilities from data. METHODS E-Synthesis is a Bayesian framework for drug safety assessments built on philosophical principles and considerations. It aims to aggregate all the available information, in order to provide a Bayesian probability of a drug causing an adverse reaction. AI systems are being developed for evidence aggregation in medicine, which increasingly are automated. RESULTS We find that AI can help E-Synthesis with information retrieval, usability (graphical decision-making aids), learning Bayes factors from historical data, assessing quality of information and determining conditional probabilities for the so-called 'indicators' of causation for E-Synthesis. Vice versa, E-Synthesis offers a solid methodological basis for (semi-)automated evidence aggregation with AI systems. CONCLUSIONS Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practice.
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Affiliation(s)
- Francesco De Pretis
- Department of Biomedical Sciences and Public Health, School of Medicine and Surgery, Marche Polytechnic University, Ancona, Italy.,Department of Communication and Economics, University of Modena and Reggio Emilia, Reggio Emilia, Italy
| | - Jürgen Landes
- Munich Center for Mathematical Philosophy, Faculty of Philosophy, Philosophy of Science and Study of Religion, Ludwig-Maximilians-Universität München, Munich, Germany
| | - William Peden
- Erasmus Institute for Philosophy and Economics, Erasmus School of Philosophy, Erasmus University Rotterdam, Rotterdam, The Netherlands.,Department of Philosophy, Durham University, Durham, UK
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McComb M, Bies R, Ramanathan M. Machine learning in pharmacometrics: Opportunities and challenges. Br J Clin Pharmacol 2021; 88:1482-1499. [PMID: 33634893 DOI: 10.1111/bcp.14801] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 02/08/2021] [Accepted: 02/12/2021] [Indexed: 12/13/2022] Open
Abstract
The explosive growth in medical devices, imaging and diagnostics, computing, and communication and information technologies in drug development and healthcare has created an ever-expanding data landscape that the pharmacometrics (PMX) research community must now traverse. The tools of machine learning (ML) have emerged as a powerful computational approach in other data-rich disciplines but its effective utilization in the pharmaceutical sciences and PMX modelling is in its infancy. ML-based methods can complement PMX modelling by enabling the information in diverse sources of big data, e.g. population-based public databases and disease-specific clinical registries, to be harnessed because they are capable of efficiently identifying salient variables associated with outcomes and delineating their interdependencies. ML algorithms are computationally efficient, have strong predictive capabilities and can enable learning in the big data setting. ML algorithms can be viewed as providing a computational bridge from big data to complement PMX modelling. This review provides an overview of the strengths and weaknesses of ML approaches vis-à-vis population methods, assesses current research into ML applications in the pharmaceutical sciences and provides perspective for potential opportunities and strategies for the successful integration and utilization of ML in PMX.
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Affiliation(s)
- Mason McComb
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA
| | - Robert Bies
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Institute for Computational Data Science, University at Buffalo, NY, USA
| | - Murali Ramanathan
- Department of Pharmaceutical Sciences, University at Buffalo, University at Buffalo, State University of New York, Buffalo, NY, USA.,Department of Neurology, University at Buffalo, State University of New York, Buffalo, NY, USA
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Storbeck KH, Schiffer L, Baranowski ES, Chortis V, Prete A, Barnard L, Gilligan LC, Taylor AE, Idkowiak J, Arlt W, Shackleton CHL. Steroid Metabolome Analysis in Disorders of Adrenal Steroid Biosynthesis and Metabolism. Endocr Rev 2019; 40:1605-1625. [PMID: 31294783 PMCID: PMC6858476 DOI: 10.1210/er.2018-00262] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 06/04/2019] [Indexed: 01/01/2023]
Abstract
Steroid biosynthesis and metabolism are reflected by the serum steroid metabolome and, in even more detail, by the 24-hour urine steroid metabolome, which can provide unique insights into alterations of steroid flow and output indicative of underlying conditions. Mass spectrometry-based steroid metabolome profiling has allowed for the identification of unique multisteroid signatures associated with disorders of steroid biosynthesis and metabolism that can be used for personalized approaches to diagnosis, differential diagnosis, and prognostic prediction. Additionally, steroid metabolome analysis has been used successfully as a discovery tool, for the identification of novel steroidogenic disorders and pathways as well as revealing insights into the pathophysiology of adrenal disease. Increased availability and technological advances in mass spectrometry-based methodologies have refocused attention on steroid metabolome profiling and facilitated the development of high-throughput steroid profiling methods soon to reach clinical practice. Furthermore, steroid metabolomics, the combination of mass spectrometry-based steroid analysis with machine learning-based approaches, has facilitated the development of powerful customized diagnostic approaches. In this review, we provide a comprehensive up-to-date overview of the utility of steroid metabolome analysis for the diagnosis and management of inborn disorders of steroidogenesis and autonomous adrenal steroid excess in the context of adrenal tumors.
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Affiliation(s)
- Karl-Heinz Storbeck
- Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
| | - Lina Schiffer
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
| | - Elizabeth S Baranowski
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
- Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, United Kingdom
- Department of Paediatric Endocrinology and Diabetes, Birmingham Women’s and Children’s Hospital NHS Foundation Trust, Birmingham, United Kingdom
| | - Vasileios Chortis
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
- Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, United Kingdom
- Department of Endocrinology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Alessandro Prete
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
- Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, United Kingdom
- Department of Endocrinology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Lise Barnard
- Department of Biochemistry, Stellenbosch University, Stellenbosch, South Africa
| | - Lorna C Gilligan
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
| | - Angela E Taylor
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
| | - Jan Idkowiak
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
- Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, United Kingdom
- Department of Paediatric Endocrinology and Diabetes, Birmingham Women’s and Children’s Hospital NHS Foundation Trust, Birmingham, United Kingdom
| | - Wiebke Arlt
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
- Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, United Kingdom
- Department of Endocrinology, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, United Kingdom
| | - Cedric H L Shackleton
- Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, United Kingdom
- UCSF Benioff Children’s Hospital Oakland Research Institute, Oakland, California
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