Context
The advent of Artificial Intelligence (AI) has opened up a world of possibilities with respect to fast-tracking drug development.
About the Process of Drug Development
- Target Identification and Validation: The first step in drug development is identifying and validating a biological target, usually a protein, that the drug will interact with. A protein is considered a suitable drug target if it can bind with potential drug molecules at specific sites, making it “druggable.”
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Examples of New Drugs Formulated through AI:
- DSP-1181: For obsessive-compulsive disorder.
- Halicin: For antibiotic resistance
- BMS-986195: A potential treatment for fibrosis.
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- Discovery Phase: During the discovery phase, computational methods analyze the target protein’s sequence to identify the best drug molecule from a vast digital library. Computer models predict drug binding sites on the protein, reducing the need for time-consuming lab methods.
- Pre-Clinical Testing: Once a potential drug is identified, it enters the pre-clinical phase. Here the drug is tested on cell cultures and animal models to evaluate safety, toxicity, efficacy, and potential adverse effects before human trials.
- Clinical Trials: Clinical trials test the drug on humans in multiple phases. First, a small group receives the drug to evaluate safety and tolerance. Then, a larger group is tested to monitor efficacy and further assess safety.
- Regulatory Approval and Marketing: After successful clinical trials, the drug undergoes a rigorous review by regulatory bodies to ensure safety and effectiveness. Once approved, it is marketed to healthcare providers and the public.
About Deep Neural Network (DNN)
- Deep learning neural networks, or artificial neural networks, attempt to mimic the human brain through a combination of data inputs, weights, and biases.
- Neural Network comprises numerous small nodes akin to brain neurons. When hit by a stimulus, the system activates processes in these nodes, which are generally grouped into layers.
Generative AI drugs:
- It refers to medicines developed using AI techniques that can generate new molecular structures and predict their efficacy, safety, and potential side effects.
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- Post-Market Surveillance: After the drug’s release, ongoing surveillance monitors its real-world effectiveness and any long-term side effects not evident during clinical trials.
Role of AI in Drug Development and Discovery
- Enhanced Target Discovery: AI, particularly through advanced tools like AlphaFold and RoseTTAFold, revolutionizes target discovery by accurately predicting the three-dimensional structures of proteins, DNA, and RNA. This ability allows for a more precise understanding of how drugs can interact with these biological targets.
- Improved Accuracy and Efficiency: AI models drastically reduce the time required for drug-target interaction studies and increase the accuracy of these predictions.
- For instance, AlphaFold 3 predicted drug-target interactions with a 76% accuracy rate in tests, a substantial improvement over previous methods.
- Cost Reduction: By leveraging deep neural networks and generative diffusion-based architectures (a kind of AI model), AI minimizes the need for expensive and time-consuming laboratory experiments, thus reducing the drug development cost.
- Versatility in Predictions: The latest advancements in AI tools, such as AlphaFold 3 and RoseTTAFold All-Atom, extend beyond predicting static protein structures. They can now predict interactions involving any combination of protein, DNA, RNA, small molecules, and ions, which broadens the scope of drug development research.
Limitations of Usage of AI in Drug Development
- Limited Prediction Accuracy: AI tools usually achieve up to 80% accuracy in predicting drug-target interactions. However, this accuracy drops significantly for more complex interactions, like protein-RNA, revealing the challenges in modeling intricate biological phenomena.
- Restricted Application in Drug Development Phases: AI tools enhance target discovery and drug-target interactions but don’t affect pre-clinical and clinical trials. Thus, AI-identified drug candidates still need traditional testing and have no guaranteed success in later stages.
- Model Hallucinations: Diffusion-based AI models can experience “model hallucinations,” generating incorrect or non-existent predictions due to insufficient or poor-quality training data, which limits output reliability.
- Restricted Access to Advanced Tools: Unlike earlier versions, advanced tools like AlphaFold 3 are not publicly available. This restricts independent verification and broader use, especially for critical studies like protein-small molecule interactions.
Limited use of AI for Drug Development in India
- Lack of Advanced Computing Infrastructure: India needs extensive computing resources, like high-speed GPUs, for AI-driven drug development. These GPUs are costly and quickly become outdated due to rapid technological advancements.
- Shortage of Skilled AI Professionals: There is a significant gap in the availability of skilled AI scientists in India compared to countries like the U.S. and China. This shortage hinders the capacity to innovate and develop new AI tools within the country.
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Conclusion
India’s position as the “Pharmacy of the World” could be strengthened with increased usage of AI in drug development. With a growing number of pharmaceutical organizations, India can lead the way in applying AI tools in target discovery, identification, and drug testing.
May 18, 2024