AI in Drug Discovery and Clinical Trials.pptx

AI IN DRUG DISCOVERY AND CLINICAL TRIALS
This presentation delves into the emerging applications of artificial intelligence (AI) in the drug discovery process and clinical trial management, highlighting the transformative potential of this technology in the pharmaceutical industry.

1. AI IN DRUG DISCOVERY AND CLINICAL TRIALS This presentation delves into the emerging applications of artificial intelligence (AI) in the drug discovery process and clinical trial management, highlighting the transformative potential of this technology in the pharmaceutical industry.

2. INTRODUCTION TO AI IN DRUG DISCOVERY Target Identification and Validation AI-powered tools can accelerate the process of identifying and validating potential drug targets, analyzing complex biological data to uncover novel therapeutic opportunities. Virtual Screening and Drug Candidate Selection AI algorithms can rapidly screen millions of drug-like compounds, identify promising candidates, and optimize their properties for improved efficacy and safety. Predictive Modeling and Simulations AI models can predict the pharmacokinetics, pharmacodynamics, and toxicity of drug candidates, enabling faster and more efficient drug development. Streamlining Experimental Workflows AI-powered automation and robotics can optimize experimental processes, reducing time and costs, while improving data quality and reproducibility. Personalized Medicine and Precision Diagnostics AI techniques can aid in the development of personalized treatments, identify biomarkers, and stratify patient populations for targeted therapies.

3. KEY MILESTONES IN AI-DRIVEN DRUG DISCOVERY 2012 AlphaFold, a deep learning model by DeepMind, achieves remarkable accuracy in predicting protein structures. 2016 Atomwise launches its AI-powered platform for virtual drug screening, identifying novel drug candidates. 2018 IBM Watson for Drug Discovery unveils its AI system to accelerate the identification of new drug targets and repurpose existing drugs. 2019 Exscientia develops the first AI-designed drug candidate to enter human clinical trials. 2020 Insilico Medicine's AI system identifies a novel COVID-19 drug candidate and advances it to clinical trials in record time.

4. “THE FUTURE OF DRUG DISCOVERY IS IN AI.” SUNDAR PICHAI, CEO OF GOOGLE

5. YANN LECUN Yann LeCun is a French computer scientist and professor at New York University. He is known as one of the pioneers of deep learning, a key technique in artificial intelligence that has revolutionized fields like drug discovery. LeCun was the founding director of the artificial intelligence research lab at Facebook and is currently the chief AI scientist at Meta.

6. AI DRUG DISCOVERY EXPERTS Dr. Emily Chen Chief Scientific Officer, Insilico Medicine Dr. Markus Spleiss VP of AI Research, Atomwise Dr. Sarah Weissman Director of Computational Biology, Recursion Pharmaceuticals Dr. Rohan Sharma Head of Machine Learning, Exscientia

7. RECURSION PHARMACEUTICALS Recursion Pharmaceuticals is a biotechnology company that uses artificial intelligence and machine learning to map the human body at the cellular level and identify new treatments for diseases.

8. AI-POWERED DRUG REPURPOSING AI Algorithm Analyzing Drug Data An AI algorithm scanning through vast amounts of drug data, including chemical structures, molecular targets, and clinical trial information, to identify potential new applications for existing drugs. Molecular Structure Visualization A 3D visualization of the molecular structure of a drug, which the AI uses to assess its potential for repurposing by analyzing its interactions with other biological targets. Clinical Trial Data Mining The AI system combing through historical clinical trial data to uncover patterns and insights that could suggest new indications for existing drugs, based on their observed effects in previous studies. Drug Repositioning Dashboard A user-friendly dashboard displaying the AI's findings, highlighting promising drug candidates for repurposing and providing detailed information on their potential new applications.

9. CHALLENGES AND OPPORTUNITIES IN AI DRUG DISCOVERY Advantages of AI in Drug Discovery Limitations of AI in Drug Discovery Accelerated drug candidate identification and evaluation through high-throughput screening and computational modeling. Dependence on the quality and quantity of training data, which can be limited in the pharmaceutical industry. Improved prediction of drug-target interactions, pharmacokinetics, and toxicity. Difficulty in interpreting the complex and opaque decision-making processes of some AI models, which can hinder trust and regulatory approval. *Adapted from a review article by Vamathevan et al. (2019) in Nature Reviews Drug Discovery.

10. COLLABORATIVE EFFORTS IN AI DRUG DISCOVERY

11. THE AI DRUG DISCOVERY PIPELINE Target Identification Utilize AI algorithms to analyze massive datasets and identify promising drug targets, such as proteins or genes, that are linked to a specific disease or condition. Compound Screening Apply virtual screening techniques with AI-powered molecular docking simulations to rapidly screen millions of chemical compounds and identify promising drug candidates that are likely to bind to the target. Lead Optimization Employ AI-driven molecular design and structure-activity relationship (SAR) analysis to refine and optimize the lead compounds, improving their potency, selectivity, and drug-like properties. Preclinical Testing Leverage AI models to predict the pharmacokinetics, toxicity, and efficacy of the lead compounds in preclinical studies, accelerating the evaluation of drug candidates before clinical trials. Clinical Trials Use AI-based algorithms to design more efficient and adaptive clinical trials, identify suitable patient populations, and monitor patient outcomes and safety data throughout the different phases of clinical development.

12. AI-ASSISTED CLINICAL TRIALS Patient Recruitment Leveraging AI algorithms to identify eligible patients for clinical trials based on complex inclusion/exclusion criteria, medical history, and demographic data, leading to faster and more targeted recruitment. Adaptive Trial Design Applying AI techniques to dynamically adjust trial parameters, such as dosage, endpoints, and patient allocation, enabling more efficient and informed decision-making during the trial process. Adverse Event Monitoring Utilizing AI-powered predictive models to detect and monitor potential adverse events in real-time, allowing for early intervention and improved safety monitoring. Endpoint Optimization Employing AI to analyze data from previous trials and identify the most relevant and sensitive endpoints, leading to more accurate and efficient trial outcomes. Predictive Modeling Developing AI-driven predictive models to forecast trial success, patient responses, and optimal treatment regimens, informing crucial decision-making throughout the clinical trial lifecycle.

13. AI VS. TRADITIONAL DRUG DISCOVERY Comparison of success rates and cost per drug developed for AI-driven vs. traditional drug discovery methods Success Rate 25% Cost per Drug Developed 75% Time to Market 50% Target Validation Accuracy 85%