Training the next generation of pharmacometric modelers: a multisector perspective

Bonate, Peter L.; Barrett, Jeffrey S.; Ait‐Oudhia, Sihem; Brundage, Richard C.; Corrigan, Brian; Duffull, Stephen B.; Gastonguay, Marc R.; Karlsson, Mats O.; Kijima, Shinichi; Krause, Andreas; Lovern, Mark; Neely, Michael; Ouellet, Danièle; Plan, Elodie L.; Rao, Gauri G.; Standing, Joseph F.; Wilkins, Justin; Zhu, Hao

doi:10.1007/s10928-023-09878-4

Cited by 6 publications

(4 citation statements)

References 52 publications

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“…We also found odd code snippets, e.g., NONMEM does not have $𝑃𝐴𝑅𝐴𝑀 code block. We were surprised at the overall quality of the syllabus outline generated by ChatGPT in Case Study 1, given that pharmacometrics is a niche sub-specialty in the pharmaceutical industry, and there are only a few academic institutions that conduct cutting-edge research projects or provide training with NONMEM (16).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of ChatGPT and Gemini Large Language Models for Pharmacometrics with NONMEM

Shin,

Yu,

Bies

et al. 2024

Preprint

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Purpose To assess the ChatGPT 4.0 (ChatGPT) and Gemini Ultra 1.0 (Gemini) large language models on tasks relevant to NONMEM coding in pharmacometrics and clinical pharmacology settings. Methods ChatGPT and Gemini performance on tasks mimicking real-world applications of NONMEM was assessed. The tasks ranged from providing a curriculum for learning NONMEM and an overview of NONMEM code structure to generating code. Prompts to elicit NONMEM code for a linear pharmacokinetic (PK) model with oral administration and a more complex one-compartment model with two parallel first-order absorption mechanisms were investigated. The prompts for all tasks were presented in lay language. The code was carefully reviewed for errors by two experienced NONMEM experts, and the revisions needed to run the code successfully were identified. Results ChatGPT and Gemini provided useful NONMEM curriculum structures combining foundational knowledge with advanced concepts (e.g., covariate modeling and Bayesian approaches) and practical skills, including NONMEM code structure and syntax. Large language models (LLMs) provided an informative summary of the NONMEM control stream structure and outlined the key NM-TRAN records needed. ChatGPT and Gemini were able to generate applicable code blocks for the NONMEM control stream from the lay language prompts for the three coding tasks. The control streams contained focal structural and NONMEM syntax errors that required revision before they could be executed without errors and warnings. Conclusions LLMs may be useful in pharmacometrics for efficiently generating an initial coding template for modeling projects. However, the output can contain errors that require correction.

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Section: Discussionmentioning

confidence: 99%

Evaluation of ChatGPT and Gemini Large Language Models for Pharmacometrics with NONMEM

Shin,

Yu,

Bies

et al. 2024

Preprint

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“…This paradigm shift in pharmacometrics reverberates across the broader pharmaceutical industry. Professionals in this sector are poised to transition into the role of model interpreters as ML algorithms progressively shoulder the computational workload [ 199 ]. Looking forward, the role of ML in this field is set for significant evolution.…”

Section: Artificial Intelligence: Integration Of Machine Learning In ...mentioning

confidence: 99%

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

Marques,

Costa,

Pereira

et al. 2024

Pharmaceutics

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The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient’s uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the “five rights”: the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug–drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

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“…Every new innovator drug application submitted to the United States Food and Drug Administration contains PK-PD and population modeling data. PK-PD analyses requires individuals with specialized multi-disciplinary training and utilizes complex software tools with steep learning curves 5,6 . The model development processes require high levels of human intervention, and the interpretation of results requires expertise and experience.…”

Section: Introductionmentioning

confidence: 99%

“…The model development processes require high levels of human intervention, and the interpretation of results requires expertise and experience. There is a shortage of quali ed manpower for the PK-PD analyst workforce needs of industry, regulatory agencies, and academia 5,6 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model

Shin,

Ramanathan

2023

J Pharmacokinet Pharmacodyn

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To systematically assess the ChatGPT large language model on diverse tasks relevant to pharmacokinetic data analysis. MethodsChatGPT was evaluated with prototypical tasks related to report writing, code generation, noncompartmental analysis, and pharmacokinetic word problems. The writing task consisted of writing an introduction for this paper from a draft title. The coding tasks consisted of generating R code for semilogarithmic graphing of concentration-time pro les and calculating area under the curve and area under the moment curve from time zero to in nity. Pharmacokinetics word problems on single intravenous, extravascular bolus, and multiple dosing were taken from a pharmacokinetics textbook. Chain-of-thought and problem separation were assessed as prompt engineering strategies when errors occurred. ResultsChatGPT showed satisfactory performance on the report writing, code generation tasks and provided accurate information on the principles and methods underlying pharmacokinetic data analysis. However, ChatGPT had high error rates in numerical calculations involving exponential functions. The outputs generated by ChatGPT were not reproducible: the precise content of the output was variable albeit not necessarily erroneous for different instances of the same prompt. Incorporation of prompt engineering strategies reduced but did not eliminate errors in numerical calculations. ConclusionsChatGPT has the potential to become a powerful productivity tool for writing, knowledge encapsulation, and coding tasks in pharmacokinetic data analysis. The poor accuracy of ChatGPT in numerical calculations require resolution before it can be reliably used for PK and pharmacometrics data analysis.

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Training the next generation of pharmacometric modelers: a multisector perspective

Cited by 6 publications

References 52 publications

Evaluation of ChatGPT and Gemini Large Language Models for Pharmacometrics with NONMEM

Evaluation of ChatGPT and Gemini Large Language Models for Pharmacometrics with NONMEM

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model

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