ArticleCPT: pharmacometrics & systems pharmacology2025
Precision Dosing in Presence of Multiobjective Therapies by Integrating Reinforcement Learning and PK-PD Models: Application to Givinostat Treatment of Polycythemia Vera.
Article in CPT: pharmacometrics & systems pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Model-informed precision dosing of carboplatin in cancer patients by leveraging myelosuppression data from electronic health records.British journal of clinical pharmacology · 2026Article
- Model-Informed Deep Q-Networks to Guide Infliximab Dosing in Pediatric Crohn's Disease.Clinical pharmacology and therapeutics · 2026Article
- Toward adaptive therapeutic timing: integration of mechanistic pharmacology and artificial intelligence in precision dosing.Frontiers in pharmacology · 2026Review
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Authors and funding
3 authors.
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Abstract
Precision dosing aims to optimize and customize pharmacological treatment at the individual level. The integration of pharmacometric models with Reinforcement Learning (RL) algorithms is currently under investigation to support the personalization of adaptive dosing therapies. In this study, this hybrid technique is applied to the real multiobjective precision dosing problem of givinostat treatment in polycythemia vera (PV) patients. PV is a chronic myeloproliferative disease with an overproduction of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). The therapeutic goal is to simultaneously normalize the levels of these efficacy/safety biomarkers, thus inducing a complete hematological response (CHR). An RL algorithm, Q-Learning (QL), was integrated with a PK-PD model describing the givinostat effect on PLT, WBC, and HCT to derive both an adaptive dosing protocol (QL
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