Results 221 to 230 of about 419,601 (272)

Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model

CPT: Pharmacometrics &Systems Pharmacology, Volume 14, Issue 3, Page 486-494, March 2025.
Abstract The pharmaceutical industry has increasingly adopted model‐informed drug discovery and development (MID3) to enhance productivity in drug discovery and development. Quantitative systems pharmacology (QSP), which integrates drug action mechanisms and disease complexities to predict clinical endpoints and biomarkers is central to MID3.
Hiroaki Iwata, Ryuta Saito
wiley   +1 more source

Gradient boosting: A computationally efficient alternative to Markov chain Monte Carlo sampling for fitting large Bayesian spatio-temporal binomial regression models. [PDF]

Infect Dis Model
Huang R, McMahan C, Herrin B, McLain A, Cai B, Self S.   +5 more
europepmc   +1 more source

Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma

CPT: Pharmacometrics &Systems Pharmacology, Volume 14, Issue 3, Page 540-550, March 2025.
ABSTRACT We employed a mechanistic learning approach, integrating on‐treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post‐progression survival (PPS)—the duration from the time of documented disease progression to death—and overall survival (OS) in Head and Neck Squamous Cell ...
Kevin Atsou, Anne Auperin, Jôel Guigay, Sébastien Salas, Sebastien Benzekry   +4 more
wiley   +1 more source

Postmarketing Assessment of Antibody–Drug Conjugates: Proof‐of‐Concept Using Model‐Based Meta‐Analysis and a Clinical Utility Index Approach

CPT: Pharmacometrics &Systems Pharmacology, EarlyView.
ABSTRACT Antibody–drug conjugates (ADCs) are a promising class of targeted cancer therapies. However, they need careful dose optimization to maximize effectiveness and minimize side effects. Sometimes, safety issues may only become apparent after approval, so ongoing evaluation is important.
Innocent Gerald Asiimwe, Nour Chtiba, Samer Mouksassi, Goonaseelan (Colin) Pillai, Raimund M. Peter, Eunice Yuen, Venkatesh Pilla Reddy   +6 more
wiley   +1 more source

A Combined Model‐Based Meta‐Analysis of Aggregated and Individual FEV1 Data From Randomized COPD Trials

CPT: Pharmacometrics &Systems Pharmacology, EarlyView.
ABSTRACT Model‐based meta‐analysis allows integration of aggregated‐level data (AD) from different clinical trials in one model to assess population efficacy/safety. However, AD is limited in individual‐level information, while individual‐patient‐level data (IPD) are hard to obtain. Combined modeling may take advantage of both sources.
Liang Yang, Carolina Llanos‐Paez, Shuying Yang, Claire Ambery, Alienor Berges, Maria C. Kjellsson, Mats O. Karlsson   +6 more
wiley   +1 more source

Author Correction: Markov chain-based impact analysis of the pandemic Covid-19 outbreak on global primary energy consumption mix. [PDF]

Sci Rep
Ahmad H, Liaqat R, Alhussein M, Muqeet HA, Aurangzeb K, Ashraf HM.   +5 more
europepmc   +1 more source

DIRECT PRODUCT BRANCHING PROCESSES AND RELATED MARKOV CHAINS

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Samuel Karlin, James McGregor
openalex   +1 more source

A Bayesian Approach to Compare Accumulating Survival Data From Clinical Practice With RCT Data: A Case Study in Non‐Small Cell Lung Cancer Patients

CPT: Pharmacometrics &Systems Pharmacology, EarlyView.
ABSTRACT Survival outcomes observed in randomized controlled trials (RCTs) may not always be generalizable to clinical practice. Evaluating whether treatment outcomes in clinical practice are similar to those in RCTs shortly after a new medicine is introduced is important for making informed decisions.
Marjon V. Verschueren, Daniel V. Verschueren, Ewoudt M. W. van de Garde, Lourens T. Bloem   +3 more
wiley   +1 more source

Predicting Colloidal Interaction Parameters from Small-Angle X-ray Scattering Curves Using Artificial Neural Networks and Markov Chain Monte Carlo Sampling. [PDF]

JACS Au
Wong K, Qi R, Yang Y, Luo Z, Guldin S, Butler KT.   +5 more
europepmc   +1 more source

Building Hybrid Pharmacometric‐Machine Learning Models in Oncology Drug Development: Current State and Recommendations

CPT: Pharmacometrics &Systems Pharmacology, EarlyView.
ABSTRACT 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 are needed to ensure transparency, rigor, and effective communication for broader adoption.
Anna Fochesato, Logan Brooks, Omid Bazgir, Philippe B. Pierrillas, Candice Jamois, James Lu, Francois Mercier   +6 more
wiley   +1 more source