We propose a Buckley James (BJ) Boost Q learning framework for estimating optimal dynamic treatment regimes under right censored survival data, tailored for longitudinal randomized clinical trial settings. The method integrates accelerated failure time models with iterative boosting techniques, including componentwise least squares and regression trees, within a counterfactual Q learning framework. By directly modeling conditional survival time, BJ Boost Q learning avoids the restrictive proportional hazards assumption and enables unbiased estimation of stage specific Q functions. Grounded in potential outcomes, this framework ensures identifiability of the optimal treatment regime under standard causal assumptions. Compared to Cox based Q learning, which relies on hazard modeling and may suffer from bias under misspecification, our approach provides robust and flexible estimation. Simulation studies and analysis of the ACTG175 HIV trial demonstrate that BJ Boost Q learning yields higher accuracy in treatment decision making, especially in multistage settings where bias can accumulate.

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations. Current models rely on structural or target-based features to identify high-efficacy, low-toxicity drug combinations. However, these approaches fail to incorporate the multimodal data necessary for accurate, clinically-relevant predictions. Here, we introduce MADRIGAL, a multimodal AI model that learns from structural, pathway, cell viability, and transcriptomic data to predict drug combination effects across 953 clinical outcomes and 21842 compounds, including combinations of approved drugs and novel compounds in development. MADRIGAL uses a transformer bottleneck module to unify preclinical drug data modalities while handling missing data during training and inference--a major challenge in multimodal learning. It outperforms single-modality methods and state-of-the-art models in predicting adverse drug interactions. MADRIGAL performs virtual screening of anticancer drug combinations and supports polypharmacy management for type II diabetes and metabolic dysfunction-associated steatohepatitis (MASH). It identifies transporter-mediated drug interactions. MADRIGAL predicts resmetirom, the first and only FDA-approved drug for MASH, among therapies with the most favorable safety profile. It supports personalized cancer therapy by integrating genomic profiles from cancer patients. Using primary acute myeloid leukemia samples and patient-derived xenograft models, it predicts the efficacy of personalized drug combinations. Integrating MADRIGAL with a large language model allows users to describe clinical outcomes in natural language, improving safety assessment by identifying potential adverse interactions and toxicity risks. MADRIGAL provides a multimodal approach for designing combination therapies with improved predictive accuracy and clinical relevance.

The global cost of drug discovery and development exceeds $200 billion annually. The main results of drug discovery and development are the outcomes of clinical trials, which directly influence the regulatory approval of new drug candidates and ultimately affect patient outcomes. Despite their significance, large-scale, high-quality clinical trial outcome data are not readily available to the public. Suppose a large clinical trial outcome dataset is provided; machine learning researchers can potentially develop accurate prediction models using past trials and outcome labels, which could help prioritize and optimize therapeutic programs, ultimately benefiting patients. This paper introduces Clinical Trial Outcome (CTO) dataset, the largest trial outcome dataset with around 479K clinical trials, aggregating outcomes from multiple sources of weakly supervised labels, minimizing the noise from individual sources, and eliminating the need for human annotation. These sources include large language model (LLM) decisions on trial-related documents, news headline sentiments, stock prices of trial sponsors, trial linkages across phases, and other signals such as patient dropout rates and adverse events. CTO's labels show unprecedented agreement with supervised clinical trial outcome labels from test split of the supervised TOP dataset, with a 91 F1.

This paper presents an approach that predicts the effectiveness of HIV combination therapies by simultaneously addressing several problems affecting the available HIV clinical data sets: the different treatment backgrounds of the samples, the uneven representation of the levels of therapy experience, the missing treatment history information, the uneven therapy representation and the unbalanced therapy outcome representation. The computational validation on clinical data shows that, compared to the most commonly used approach that does not account for the issues mentioned above, our model has significantly higher predictive power. This is especially true for samples stemming from patients with longer treatment history and samples associated with rare therapies. Furthermore, our approach is at least as powerful for the remaining samples.

It might sound like something out of a Hollywood sci-fi movie -- bringing a 46,000-year-old frozen worm'back to life' after digging it up in Siberia. But that is exactly what scientists revealed they had done in a landmark study published yesterday. The experts managed to'resurrect' a long-extinct roundworm from a hibernation-like state known as cryptobiosis, which allowed it to survive the harsh frozen temperatures. Scientists previously thought that roundworms could only remain in this state for less than 40 years, so the development was an eye-opening moment for the scientific world. So, how exactly did they do it?

You are free to share this article under the Attribution 4.0 International license. Researchers have harnessed a powerful artificial intelligence platform to successfully treat a patient with advanced cancer, completely halting disease progression. The development represents a big step forward in personalized medicine, they say. In this clinical study, researchers gave a patient with metastatic castration-resistant prostate cancer (MCRPC) a novel drug combination consisting of the investigational drug ZEN-3694 and enzalutamide, an approved prostate cancer drug. The research team successfully used the platform, called CURATE.AI, to continuously identify the optimal doses of each drug to result in a durable response, allowing the patient to resume a completely normal and active lifestyle.

A new auto-commentary published in SLAS Technology looks at how an emerging area of artificial intelligence, specifically the analysis of small systems-of-interest specific datasets, can be used to improve drug development and personalized medicine. The auto-commentary builds on a study recently published by the authors in Science Translational Medicine about an artificial intelligence (AI) platform, Quadratic Phenotypic Optimization Platform (QPOP), that substantially improves combination therapy in bortezomib-resistant multiple myeloma to identify the best drug combinations for individual multiple myeloma patients. It is now evident that complex diseases, such as cancer, often require effective drug combinations to make any significant therapeutic impact. As the drugs in these combination therapies become increasingly specific to molecular targets, designing effective drug combinations as well as choosing the right drug combination for the right patient becomes more difficult. Artificial intelligence is having a positive impact on drug development and personalized medicine. With the ability to efficiently analyze small datasets that focus on the specific disease of interest, QPOP and other small dataset-based AI platforms can rationally design optimal drug combinations that are effective and based on real experimental data and not mechanistic assumptions or predictive modeling.

A new auto-commentary published in SLAS Technology looks at how an emerging area of artificial intelligence, specifically the analysis of small systems-of-interest specific datasets, can be used to improve drug development and personalized medicine. The auto-commentary builds on a study recently published by the authors in Science Translational Medicine about an artificial intelligence (AI) platform, Quadratic Phenotypic Optimization Platform (QPOP), that substantially improves combination therapy in bortezomib-resistant multiple myeloma to identify the best drug combinations for individual multiple myeloma patients. It is now evident that complex diseases, such as cancer, often require effective drug combinations to make any significant therapeutic impact. As the drugs in these combination therapies become increasingly specific to molecular targets, designing effective drug combinations as well as choosing the right drug combination for the right patient becomes more difficult. Artificial intelligence is having a positive impact on drug development and personalized medicine. With the ability to efficiently analyze small datasets that focus on the specific disease of interest, QPOP and other small dataset-based AI platforms can rationally design optimal drug combinations that are effective and based on real experimental data and not mechanistic assumptions or predictive modeling.

SINGAPORE: In a first, scientists have used a powerful artificial intelligence (AI) platform, to successfully treat a patient with advanced cancer, completely halting disease progression. The research team successfully utilised a platform called CURATE.AI to continuously identify the optimal doses of each drug to result in a durable response, allowing the patient to resume a completely normal and active lifestyle. The patient with metastatic castration-resistant prostate cancer (MCRPC) was given a novel drug combination consisting of investigational drug ZEN-3694 and enzalutamide, an approved prostate cancer drug. "Dynamic dosing in cancer therapy is not commonly used. In fact, drug dosing changes in oncology are typically performed only to reduce toxicity," said Dean Ho, from National University of Singapore (NUS).

Artificial intelligence stopped prostate cancer from spreading in a patient with an advanced form of the disease, new research shows. An unnamed patient with tumours that had spread outside his prostate was given a combination of an experimental drug and an approved cancer medication. AI technology, known as CURATE.AI, was used to continuously assess how well the patient responded to the drugs, with the medications' doses being adjusted accordingly. These dose changes reduced cancer markers in his blood to the lowest levels they had ever been, with CT scans also revealing the patient's tumours had not spread further. Lead author Professor Dean Ho, from the National University of Singapore, said: 'The unique ability for CURATE.AI to rapidly identify the drug doses that result in the best possible treatment outcomes allows for optimised personalised medicine.