Shahar Harel, Head of AI at Quris, presented a BIO-AI approach to drug safety assessment using a 'patient-on-a-chip' platform. This platform simulates the human body and generates high-frequency microscopy and biochemical data on drug interactions, considering patient genomics and ethnicity. The data is used to train multimodal deep learning models to predict drug safety and provide patient-specific recommendations. Why it matters: This approach offers a potential alternative to animal models, promising faster and more personalized drug development while reducing safety concerns.
MBZUAI's Professor Le Song is developing an AI-driven simulation to model the human body at societal, organ, tissue, cellular, and molecular levels. The goal is to reduce the time and cost associated with bringing new medicines to market by removing the need for wet lab biological research. Song aims to create a comprehensive model using machine learning. Why it matters: This research could revolutionize drug discovery in the region by accelerating the development process and reducing reliance on traditional research methods.
MBZUAI hosted a two-day workshop on "Big Model AI in Drug Design" starting February 20, 2023. The workshop featured presentations from researchers in public and private institutions working on AI and health. MBZUAI Adjunct Professor Eran Segal opened the workshop with a talk on the Human Phenotype Project. Why it matters: The event highlights the growing interest and activity in applying AI, particularly large models, to advance drug discovery and personalized medicine within the UAE's research ecosystem.
MBZUAI's Kun Zhang is applying causal machine learning to improve drug development and precision medicine, focusing on answering 'why' questions. Traditional drug development is costly (est. $2B) due to extensive studies needed to determine drug toxicity and efficacy. Zhang is combining causal ML with organs-on-chips technology to improve pre-clinical drug testing, aiming to reduce the failure rate of drugs in human trials. Why it matters: By improving the accuracy of pre-clinical drug testing, this research could significantly reduce the cost and time required to bring new medicines to market in the region and worldwide.
KAUST researchers led by Dr. Niveen Khashab have developed thermosensitive liposomes for controlled drug release, particularly in cancer therapies. The liposomes are designed to release drugs only when they reach heated tumor tissue, minimizing systemic side effects. Cholesterol moieties are used as anchors to create a "nail" or "comb" effect, enabling temperature-triggered drug release inside cells. Why it matters: This targeted drug delivery system could significantly improve the efficacy and reduce the toxicity of cancer treatments.
KAUST's Laboratory of Stem Cells and Diseases, led by Assistant Professor Antonio Adamo, uses induced pluripotent stem cells (iPSCs) to model diseases like diabetes. The lab employs a reprogramming technique to revert patient fibroblasts into iPSCs, enabling the study of disease progression in vitro. Adamo's research focuses on enzymes and disregulated transcriptional/epigenetic mechanisms to understand disease onset. Why it matters: This research contributes to regenerative medicine and offers insights into metabolic diseases relevant to the GCC region.
Khaled Alsayegh at the King Abdullah International Medical Research Center is creating a Saudi Stem Cell Donor Registry, with 80,000 potential donors identified. The aim is to identify universal donors, reprogram their cells into induced pluripotent stem (iPS) cells, and create a gene bank for matched tissue transplants. Alsayegh is collaborating with Jesper Tegnér at KAUST to create pacemaker cells using single-cell RNA sequencing. Why it matters: This initiative could revolutionize precision medicine in KSA by providing readily available, matched cells for transplants, reducing the need for patient-specific reprogramming and improving treatment outcomes.
Natasa Przulj at the Barcelona Supercomputing Center is developing an AI framework that fuses multi-omic data to improve precision medicine. The framework uses graph-regularized non-negative matrix tri-factorization (NMTF) and network science algorithms for patient stratification, biomarker prediction, and drug repurposing. It is applied to diseases like cancer, Covid-19, and Parkinson's. Why it matters: This research can enable more personalized and effective treatments by leveraging complex biological data to understand disease mechanisms and tailor therapies.