About

David Z. D'Argenio is a Professor of Biomedical Engineering at the University of Southern California and the inaugural holder of the Chonette Chair of Biomedical Technology. He has been a member of the USC Biomedical Engineering Faculty since 1979 and also serves as an Adjunct Professor in the Department of Pharmaceutical Sciences at the University at Buffalo, SUNY. His educational background includes a Doctoral Degree in Biomedical Engineering from USC, a Master's Degree in Electrical Engineering from Pennsylvania State University, and a Bachelor's Degree in Electrical Engineering from the University of Dayton. Professor D'Argenio's research focuses on systems modeling to understand the molecular and cellular mechanisms underlying how medicines produce their effects, known as quantitative systems pharmacology (QSP), and on characterizing interpatient variability in treatment response through pharmacometrics. His work aims to develop, evaluate, and apply methods of QSP and pharmacometrics to advance model-based discovery, development, and precision medicine, leading to more effective treatments for life-threatening diseases. His contributions include quantifying mechanisms of action for various therapeutic modalities, understanding pharmacokinetics and pharmacodynamics, and incorporating genetic and epigenetic factors in therapeutic response analysis. He has developed advanced computational methods tailored for the challenging data environments of model-based discovery and therapeutics. Professor D'Argenio has held numerous leadership roles, including serving as Chairman of the Department of Biomedical Engineering at USC, Associate Dean for Academic Affairs in the USC School of Engineering, and Interim Director of the Alfred Mann Institute of BME at USC. He has been recognized with multiple awards, such as the Lewis B. Sheiner Award from the International Society of Pharmacometrics, the Gerhard Levy Distinguished Lectureship, and the Avis Distinguished Visiting Professorship. He has served on the US FDA Advisory Committee for Pharmaceutical Science and Clinical Pharmacology and has been a consultant for numerous pharmaceutical and biotechnology companies. Additionally, he was a founding member of the International Society of Pharmacometrics and served on the Board of Directors of Simulations Plus, Inc. His research also involves developing and supporting the ADAPT software system used by researchers and clinicians worldwide.

Selected publications

• Non-linear IV pharmacokinetics of the ATR inhibitor berzosertib (M6620) in mice

  Cancer Chemotherapy and Pharmacology · 2024-05-14 · 3 citations

  articleOpen access
  PublisherOA PDFDOI

• An in-silico modeling approach to separate exogenous and endogenous plasma insulin appearance, with application to inhaled insulin

  Scientific Reports · 2024-05-13 · 2 citations

  articleOpen access

  Abstract The aim of this study was to develop a dynamic model-based approach to separately quantify the exogenous and endogenous contributions to total plasma insulin concentration and to apply it to assess the effects of inhaled-insulin administration on endogenous insulin secretion during a meal test. A three-step dynamic in-silico modeling approach was developed to estimate the two insulin contributions of total plasma insulin in a group of 21 healthy subjects who underwent two equivalent standardized meal tests on separate days, one of which preceded by inhalation of a Technosphere ® Insulin dose (22U or 20U). In the 30–120 min test interval, the calculated endogenous insulin component showed a divergence in the time course between the test with and without inhaled insulin. Moreover, the supra-basal area-under-the-curve of endogenous insulin in the test with inhaled insulin was significantly lower than that in the test without (2.1 ± 1.7 × 10 4 pmol·min/L vs 4.2 ± 1.8 × 10 4 pmol·min/L, p < 0.01). The percentage of exogenous insulin reaching the plasma, relative to the inhaled dose, was 42 ± 21%. The proposed in-silico approach separates exogenous and endogenous insulin contributions to total plasma insulin, provides individual bioavailability estimates, and can be used to assess the effect of inhaled insulin on endogenous insulin secretion during a meal.

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• Long-term forecasting of a motor outcome following rehabilitation in chronic stroke via a hierarchical bayesian dynamic model

  Journal of NeuroEngineering and Rehabilitation · 2023-06-29 · 10 citations

  articleOpen access

  BACKGROUND: Given the heterogeneity of stroke, it is important to determine the best course of motor therapy for each patient, i.e., to personalize rehabilitation based on predictions of long-term outcomes. Here, we propose a hierarchical Bayesian dynamic (i.e., state-space) model (HBDM) to forecast long-term changes in a motor outcome due to rehabilitation in the chronic phase post-stroke. METHODS: The model incorporates the effects of clinician-supervised training, self-training, and forgetting. In addition, to improve forecasting early in rehabilitation, when data are sparse or unavailable, we use the Bayesian hierarchical modeling technique to incorporate prior information from similar patients. We use HBDM to re-analyze the Motor Activity Log (MAL) data of participants with chronic stroke included in two clinical trials: (1) the DOSE trial, in which participants were assigned to a 0, 15, 30, or 60-h dose condition (data of 40 participants analyzed), and (2) the EXCITE trial, in which participants were assigned a 60-h dose, in either an immediate or a delayed condition (95 participants analyzed). RESULTS: For both datasets, HBDM accounts well for individual dynamics in the MAL during and outside of training: mean RMSE = 0.28 for all 40 DOSE participants (participant-level RMSE 0.26 ± 0.19-95% CI) and mean RMSE = 0.325 for all 95 EXCITE participants (participant-level RMSE 0.32 ± 0.31), which are small compared to the 0-5 range of the MAL. Bayesian leave-one-out cross-validation shows that the model has better predictive accuracy than static regression models and simpler dynamic models that do not account for the effect of supervised training, self-training, or forgetting. We then showcase model's ability to forecast the MAL of "new" participants up to 8 months ahead. The mean RMSE at 6 months post-training was 1.36 using only the baseline MAL and then decreased to 0.91, 0.79, and 0.69 (respectively) with the MAL following the 1st, 2nd, and 3rd bouts of training. In addition, hierarchical modeling improves prediction for a patient early in training. Finally, we verify that this model, despite its simplicity, can reproduce previous findings of the DOSE trial on the efficiency, efficacy, and retention of motor therapy. CONCLUSIONS: In future work, such forecasting models can be used to simulate different stages of recovery, dosages, and training schedules to optimize rehabilitation for each person. Trial registration This study contains a re-analysis of data from the DOSE clinical trial ID NCT01749358 and the EXCITE clinical trial ID NCT00057018.

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• Editorial: Model-informed decision making in the preclinical stages of pharmaceutical research and development

  Frontiers in Pharmacology · 2023-04-12 · 1 citations

  editorialOpen access

  EDITORIAL article Front. Pharmacol., 12 April 2023Sec. Experimental Pharmacology and Drug Discovery Volume 14 - 2023 | https://doi.org/10.3389/fphar.2023.1184914

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• Physiologically Based Modeling to Predict Monoclonal Antibody Pharmacokinetics in Humans from in vitro Physiochemical Properties

  mAbs · 2022-05-01 · 23 citations

  articleOpen accessSenior authorCorresponding

  measures of antibody physiochemical properties, and can be expanded to include more descriptive representations of each of the antibody processing subsystems, as well as other antibody-specific information.

  PublisherDOI

• Long-Term Forecasting of a Motor Outcome Following Rehabilitation in Chronic Stroke via a Hierarchical Bayesian Model of Motor Learning

  medRxiv · 2022-10-21 · 1 citations

  preprintOpen access

  Abstract Background Given the heterogeneity of stroke, it is important to determine the best course of motor therapy for each patient, i.e., to personalize rehabilitation based on predictions of long-term outcomes. Here, we propose a Hierarchical Bayesian dynamical (i.e., state-space) model of motor learning to forecast long-term changes in a motor outcome due to rehabilitation in the chronic phase post-stroke. Methods The model incorporates the effects of clinician-supervised training, self-training, and forgetting. In addition, to improve forecasting early in rehabilitation, when data are sparse or unavailable, we use a hierarchical Bayesian structure, which incorporates prior information from similar patients. We use this dynamical model to re-analyze Motor Activity Log (MAL) data of participants with chronic stroke included in two clinical trials: 1) the DOSE trial, in which participants were assigned to a 0, 15, 30, or 60-hour dose condition (data of 40 participants analyzed), and 2) the EXCITE trial, in which participants were assigned a 60-hour dose, in either an immediate or a delayed condition (95 participants analyzed). Results For both datasets, the dynamical model accounts well for individual trajectory in the MAL during and outside of training and better fits the data than other simpler models without the effects of either supervised training, self-training or forgetting or (static) regression models. We then show how the model can be used to forecast the MAL of new participants up to 8 months ahead and how the hierarchical structure improves the accuracy of the predictions early in training when data are sparse. Finally, we verify that this model, despite its simplicity, can reproduce previous findings of the DOSE trial on the efficiency, efficacy, and retention of motor therapy. Conclusion In future work, such forecasting models can be simulated for different stages of recovery, dosages, and training schedules to optimize rehabilitation for each person.

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• Analysis of Complex Absorption After Multiple Dosing: Application to the Interaction Between the P-glycoprotein Substrate Talinolol and Rifampicin

  Pharmaceutical Research · 2022-09-26 · 5 citations

  articleOpen access

  PURPOSE: In order to clarify the effect of rifampicin on the bioavailability of the P-glycoprotein substrate talinolol, its absorption kinetics was modeled after multiple-dose oral administration of talinolol in healthy subjects. METHODS: A sum of two inverse Gaussian functions was used to calculate the time course of the input rate into the systemic circulation. RESULTS: The estimated rate of drug entry into the systemic circulation revealed two distinct peaks at 1 and 3.5 h after administration. Rifampicin did not affect bioavailability of talinolol, but did shift the second peak of the input function by 1.3 h to later times. Elimination clearance and one of the intercompartmental distribution clearances increased significantly under rifampicin treatment. CONCLUSIONS: Rifampicin changes the time course of absorption rate but not the fraction absorbed of talinolol. The model suggests the existence of two intestinal absorption windows for talinolol.

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• Monoclonal Antibody Pharmacokinetics in Cynomolgus Monkeys Following Subcutaneous Administration: Physiologically Based Model Predictions from Physiochemical Properties

  The AAPS Journal · 2022-12-01 · 5 citations

  articleSenior author
  PublisherDOI

• Open‐source <i>maximum a posteriori</i>‐bayesian dosing AdDS to current therapeutic drug monitoring: Adapting to the era of individualized therapy

  Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · 2021-10-08 · 5 citations

  reviewOpen access

  Recent updates in the therapeutic drug monitoring (TDM) guidelines for vancomycin have rekindled interest in maximum a posteriori-Bayesian (MAP-Bayesian) estimation of patient-specific pharmacokinetic parameters. To create a versatile infrastructure for MAP-Bayesian dosing of vancomycin or other drugs, a freely available, R-based software package, Advanced Dosing Solutions (AdDS), was created to facilitate clinical implementation of these improved TDM methods. The objective of this study was to utilize AdDS for pre- and post-processing of data in order to streamline the therapeutic management of vancomycin in healthy and obese veterans. Patients from a local Veteran Affairs hospital were utilized to compare the process of full re-estimation versus Bayesian updating of priors on healthy adult and obese patient populations for use with AdDS. Twenty-four healthy veterans were utilized to train (14/24) and test (10/24) the base pharmacokinetic model of vancomycin while comparing the effects of updated and fully re-estimated priors. This process was repeated with a total of 18 obese veterans for both training (11/18) and testing (7/18). Comparison of MAP objective function between the original and re-estimated models for healthy adults indicated that 78.6% of the subjects in the training and 70.0% of the subjects in the testing datasets had similar or improved predictions by the re-estimated model. For obese veterans, 81.8% of subjects in the training dataset and 85.7% of subjects in the testing dataset had similar or improved predictions. Re-estimation of model parameters provided more significant improvements in objective function compared with Bayesian updating, which may be a useful strategy in cases where sufficient samples and subjects are available. The generation of bespoke regimens based on patient-specific clearance and minimal sampling may improve patient care by addressing fundamental pharmacokinetic differences in healthy and obese veteran populations.

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• An Analysis of Glucose Effectiveness in Subjects With or Without Type 2 Diabetes via Hierarchical Modeling

  Frontiers in Endocrinology · 2021-03-29 · 5 citations

  articleOpen accessSenior authorCorresponding

  Glucose effectiveness, defined as the ability of glucose itself to increase glucose utilization and inhibit hepatic glucose production, is an important mechanism maintaining normoglycemia. We conducted a minimal modeling analysis of glucose effectiveness at zero insulin ( GEZI ) using intravenous glucose tolerance test data from subjects with type 2 diabetes (T2D, n=154) and non-diabetic (ND) subjects (n=343). A hierarchical statistical analysis was performed, which provided a formal mechanism for pooling the data from all study subjects, to yield a single composite population model that quantifies the role of subject specific characteristics such as weight, height, age, sex, and glucose tolerance. Based on the resulting composite population model, GEZI was reduced from 0.021 min –1 (standard error – 0.00078 min –1 ) in the ND population to 0.011 min –1 (standard error – 0.00045 min –1 ) in T2D. The resulting model was also employed to calculate the proportion of the non–insulin-dependent net glucose uptake in each subject receiving an intravenous glucose load. Based on individual parameter estimates, the fraction of total glucose disposal independent of insulin was 72.8% ± 12.0% in the 238 ND subjects over the course of the experiment, indicating the major contribution to the whole-body glucose clearance under non-diabetic conditions. This fraction was significantly reduced to 48.8% ± 16.9% in the 30 T2D subjects, although still accounting for approximately half of the total in the T2D population based on our modeling analysis. Given the potential application of glucose effectiveness as a predictor of glucose intolerance and as a potential therapeutic target for treating diabetes, more investigations of glucose effectiveness in other disease conditions can be conducted using the hierarchical modeling framework reported herein.

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Awards & honors

• Avis Distinguished Visiting Professorship in Pharmaceutical…
• Lewis B. Sheiner Award from the International Society of Pha…
• Gerhard Levy Distinguished Lectureship, Univ. of Buffalo, SU…
• Fellow, International Society of Pharmacometrics (2014)
• House Ear Institute Chair of the Board of Directors (2011)