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Use of AI in Drug Development and its Drawbacks

Context

  • The drug development process is traditionally expensive and time-consuming.
  • Artificial Intelligence (AI) has introduced new possibilities to accelerate this process.

Drug Development Process

  • Identifying and Validating a Target: The process begins by identifying and validating a biological molecule (target), usually a protein or gene, which the drug will bind to.
    • Druggable proteins have specific sites where drugs can dock to exert their effects.
  • Discovery Phase: Target proteins are identified, and their sequences are fed into a computer.
    • The computer searches for the best-fitting drug from millions of stored small molecule structures.
    • This computational process bypasses time-consuming and costly laboratory experiments, reducing failure rates.
    • Once a suitable protein target and drug are identified, the research moves to the pre-clinical phase.
  • Pre-Clinical Phase: Potential drug candidates are tested on cells and animals for safety and toxicity.
  • Clinical Phase: The drug undergoes trials on a small number of human patients for initial testing, followed by more extensive patient trials for efficacy and safety.
  • Regulatory Approval and Post-Market Surveillance: The drug is reviewed for regulatory approval and, upon approval, is marketed and monitored for any post-market issues.

Role of AI in Drug Development

  • Accelerating Target Discovery and Drug-Target Interaction:
    • AI can significantly reduce the time and cost involved in drug development and increase the accuracy of predictions.
    • AI-based prediction tools like AlphaFold and RoseTTAFold have made significant advancements in computational drug development.
  • AlphaFold and RoseTTAFold:
    • Developed by DeepMind (Google) and the University of Washington, respectively.
    • These tools use deep neural networks to predict the three-dimensional structures of proteins.
    • New versions, AlphaFold 3 and RoseTTAFold All-Atom predict interactions for proteins, DNA, RNA, and small molecules with higher accuracy.
    • In tests, AlphaFold 3 predicted interactions accurately 76% of the time, compared to 40% by RoseTTAFold All-Atom.

Drawbacks of AI in Drug Development

  • Accuracy Limitations: Current Artificial Intelligence (AI) tools can achieve up to 80% accuracy in predictions, which drops significantly for protein-RNA interactions.
  • Limited to Early Phases: AI tools primarily assist in the target discovery and drug-target interaction phases, but pre-clinical and clinical phases still need to be completed without any guaranteed success.
  • Model Hallucinations: Diffusion-based architectures can produce incorrect predictions due to insufficient training data.
  • Code Availability: The code for AlphaFold 3 has not been released, limiting independent verification and broader utilisation.

Challenges and Opportunities in India

  • Infrastructure Needs: Developing AI tools requires large-scale computing infrastructure with advanced GPU chips, which are expensive and have quick expiration dates.
  • Skill Gap: India lacks the number of skilled AI scientists compared to the U.S. and China, limiting its ability to develop first-mover AI tools for drug development.
  • Potential in Pharmaceutical Applications: Despite challenges, India’s growing pharmaceutical sector has the potential to lead in applying AI tools for target discovery, identification, and drug testing.
About iOncology AI Project
Goal: Develop an AI-powered platform (iOncology AI) to help oncologists select the most effective treatment for cancer patients based on their genetic makeup.

Key Features:

  • Data Collection: Stores patients’ blood tests, lab reports, scans, and medical history.
  • Accuracy: Trained on 1,500 breast and ovarian cancer cases, achieving 75% accuracy compared to clinician diagnosis.
  • Focus Cancers: Currently trained for breast and ovarian cancers, expanding to head and neck, colorectal, and blood cancers.
  • Confidentiality: Maintains patient privacy through individual logins and anonymized data analysis.

Significance:

  • Improved Treatment Selection: Provides data-driven insights to guide oncologists in choosing the best treatment for each patient.
  • Personalised Medicine: Advances personalised medicine by considering individual genetic variations for targeted therapy.
  • Early Detection Potential: May contribute to earlier cancer detection through AI-assisted analysis of scans and reports.
  • Resource Optimization: Offers potential for effective treatment in resource-limited settings.
  • Data-Driven Research: Generates valuable data for developing prevention strategies and treatment protocols specific to the Indian population.

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