ML-enabled PK/PD review
Psarellis et al. group ML/AI in PK/PD into parameter prediction and time-course forecasting. They highlight gray-box models combining ML + mechanistic structure for better prediction, variability handling, and NAM-aligned decisions.
https://t.co/vPXKbNTN5B
π Featured Article: QSP-Copilot by Anuraag Saini & Ali Farnoud uses AI agents to extract knowledge from literature and build QSP models. With up to ~40% faster development, it accelerates QSP research.
Link https://t.co/8f3FhrSJn8β¨App https://t.co/p7ihRs4fKH
π Featured Article: Danniell Hu, βHuman at the Centerβ reframes AI beyond speed/accuracy via 3 pillars (design, user testing, ethics) + 4 humanβAI modes.
In pharmacometrics: from RMSE-driven modeling β human-centered decisions in drug development.
https://t.co/95VXfDctVE
From automating coding in pharmacometrics and complex data management to generating clinical reports with good context retention, see top models - like Claude 4.6, GPT-5.5, and Gemini 3.1 Pro that are currently dominating each stage of the pharmacometrics workflow...
π Featured article: by Ahmad, Ouahada, and Hamam on AI/ML for drug target interaction predictions.
The key question is ...can computers predict which drugs will "stick to" which proteins, before doing expensive experiments/studies?
#pharma#pharmacometrics#clinical#clinpharm
π Featured article: A commentary on AI Approach to generating MIDD assets...
This 2023 article sheds some light on how AI/ML is revolutionizing Model-Informed Drug Development.
Ref: https://t.co/TYLYPnEvD7
π Featured Article: Evaluating the Impact of AI-Based Model-Informed Drug Development
Here's a comparative review by Bingyu Mao et al that delivers an electrifying systematic analysis of how AI/ML is revolutionizing MIDD!
https://t.co/ULPb30JzwR