James Lu
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View article: TetR- and LysR-type transcriptional regulators mediate multilayered control of T3SS1 by <i>Vibrio parahaemolyticus</i> quorum sensing
TetR- and LysR-type transcriptional regulators mediate multilayered control of T3SS1 by <i>Vibrio parahaemolyticus</i> quorum sensing Open
The gram-negative pathogen Vibrio parahaemolyticus employs a sophisticated regulatory network to control virulence factors, including type III secretion system 1 (T3SS1), a key mediator of cytotoxicity. While the quorum sensing (QS) cascad…
Building Hybrid Pharmacometric‐Machine Learning Models in Oncology Drug Development: Current State and Recommendations Open
Classic and hybrid pharmacometric‐machine learning models (hPMxML) are gaining momentum for applications in clinical drug development and precision medicine, especially within the oncology therapeutic area. However, standardized workflows …
Huangkui capsule combined with finerenone attenuates diabetic nephropathy by regulating the JAK2/STAT3 signaling pathway based on network pharmacology, molecular docking, and experimental verification Open
Introduction Diabetic nephropathy (DN) is a serious complication of diabetes with limited therapeutic options. Although Huangkui capsule (HKC) and finerenone individually show potential in DN management, their combined mechanism remains un…
Shap‐Cov: An Explainable Machine Learning Based Workflow for Rapid Covariate Identification in Population Modeling Open
Covariate identification in population pharmacokinetic/pharmacodynamic (popPK/PD) modeling is a key component in model development that is often prone to bias, time‐consuming, and even intractable when too many covariates or complicated mo…
Large Language Models and Their Applications in Drug Discovery and Development: A Primer Open
Large language models (LLMs) have emerged as powerful tools in many fields, including clinical pharmacology and translational medicine. This paper aims to provide a comprehensive primer on the applications of LLMs to these disciplines. We …
View article: AI‐Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference
AI‐Driven Applications in Clinical Pharmacology and Translational Science: Insights From the ASCPT 2024 AI Preconference Open
Artificial intelligence (AI) is driving innovation in clinical pharmacology and translational science with tools to advance drug development, clinical trials, and patient care. This review summarizes the key takeaways from the AI preconfer…
Integrating real‐world data and machine learning: A framework to assess covariate importance in real‐world use of alternative intravenous dosing regimens for atezolizumab Open
The increase in the availability of real‐world data (RWD), in combination with advances in machine learning (ML) methods, provides a unique opportunity for the integration of the two to explore complex clinical pharmacology questions. Here…
Eight-Year Journey with the FIRST Program: How Robots Build Kids Open
Innovation in Science, Technology, Engineering, and Mathematics (STEM) is essential for the prosperity of the US economy and its job sectors. To equip the next generation of STEM professionals with the skills needed for innovation and to t…
Cultivating Robotic Professionals: A Learning-Practice-Service Educational Framework Open
Robotics, an interdisciplinary field spanning various science, technology, engineering, and mathematics (STEM) disciplines, is recognized as a transformative force shaping our daily lives. With its broad popularity among children and teena…
View article: Population pharmacokinetics and CD20 binding dynamics for mosunetuzumab in relapsed/refractory B‐cell non‐Hodgkin lymphoma
Population pharmacokinetics and CD20 binding dynamics for mosunetuzumab in relapsed/refractory B‐cell non‐Hodgkin lymphoma Open
Mosunetuzumab (Mosun) is a CD20xCD3 T‐cell engaging bispecific antibody that redirects T cells to eliminate malignant B cells. The approved step‐up dose regimen of 1/2/60/30 mg IV is designed to mitigate cytokine release syndrome (CRS) and…
Deep‐NCA: A deep learning methodology for performing noncompartmental analysis of pharmacokinetic data Open
Noncompartmental analysis (NCA) is a model‐independent approach for assessing pharmacokinetics (PKs). Although the existing NCA algorithms are very well‐established and widely utilized, they suffer from low accuracies in the setting of spa…
From Noise to Signal: Unveiling Treatment Effects from Digital Health Data through Pharmacology-Informed Neural-SDE Open
Digital health technologies (DHT), such as wearable devices, provide personalized, continuous, and real-time monitoring of patient. These technologies are contributing to the development of novel therapies and personalized medicine. Gainin…
Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODE Open
While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic…
View article: Population Pharmacokinetics and CD20 Binding Dynamics for Mosunetuzumab in Relapsed/Refractory B-Cell Non-Hodgkin Lymphoma (R/R NHL)
Population Pharmacokinetics and CD20 Binding Dynamics for Mosunetuzumab in Relapsed/Refractory B-Cell Non-Hodgkin Lymphoma (R/R NHL) Open
Introduction: Mosunetuzumab (Mosun) is a CD20xCD3 T-cell engaging bispecific antibody that redirects T cells to eliminate malignant B cells. Clinical data from GO29781 (NCT02500407), a Phase I/II, open-label, multicenter dose-escalation an…
Machine Learning‐Based Quantification of Patient Factors Impacting Remission in Patients With Ulcerative Colitis: Insights from Etrolizumab Phase III Clinical Trials Open
Etrolizumab, an investigational anti‐β7 integrin monoclonal antibody, has undergone evaluation for safety and efficacy in phase III clinical trials on patients with moderate to severe ulcerative colitis (UC). Etrolizumab was terminated bec…
Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic Prediction Open
In anti-cancer drug development, a major scientific challenge is disentangling the complex relationships between high-dimensional genomics data from patient tumor samples, the corresponding tumor's organ of origin, the drug targets associa…
View article: Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices
Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices Open
Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model‐informed drug …
Explainable Deep Learning for Tumor Dynamic Modeling and Overall Survival Prediction using Neural-ODE Open
While tumor dynamic modeling has been widely applied to support the development of oncology drugs, there remains a need to increase predictivity, enable personalized therapy, and improve decision-making. We propose the use of Tumor Dynamic…
Machine Learning for Exposure-Response Analysis: Methodological Considerations and Confirmation of Their Importance via Computational Experimentations Open
Exposure-response (E-R) is a key aspect of pharmacometrics analysis that supports drug dose selection. Currently, there is a lack of understanding of the technical considerations necessary for drawing unbiased estimates from data. Due to r…
View article: Risk Factors of Hyperglycemia After Treatment With the AKT Inhibitor Ipatasertib in the Prostate Cancer Setting: A Machine Learning–Based Investigation
Risk Factors of Hyperglycemia After Treatment With the AKT Inhibitor Ipatasertib in the Prostate Cancer Setting: A Machine Learning–Based Investigation Open
PURPOSE Hyperglycemia is a major adverse event of phosphatidylinositol 3-kinase/AKT inhibitor class of cancer therapeutics. Machine learning (ML) methodologies can identify and highlight how explanatory variables affect hyperglycemia risk.…
Applying interpretable machine learning workflow to evaluate exposure–response relationships for large‐molecule oncology drugs Open
The application of logistic regression (LR) and Cox Proportional Hazard (CoxPH) models are well‐established for evaluating exposure–response (E–R) relationship in large molecule oncology drugs. However, applying machine learning (ML) model…
Update to improve reproducibility and interpretability: A response to “Machine Learning for Tumor Growth Inhibition” Open
Reproducibility is an important aspect of pharmacometric research, but complications can occur when complex data management and an understanding of modeling approaches from multiple disciplines are required, such as the case when machine l…
Pharm‐AutoML: An open‐source, end‐to‐end automated machine learning package for clinical outcome prediction Open
Although there is increased interest in utilizing machine learning (ML) to support drug development, technical hurdles associated with complex algorithms have limited widespread adoption. In response, we have developed Pharm‐AutoML, an ope…
An in vitro quantitative systems pharmacology approach for deconvolving mechanisms of drug-induced, multilineage cytopenias Open
Myelosuppression is one of the most common and severe adverse events associated with anti-cancer therapies and can be a source of drug attrition. Current mathematical modeling methods for assessing cytopenia risk rely on indirect measureme…