GS Paper III

News Excerpt:

As the Indian pharmaceutical industry stands at the crossroads of tradition and technology, Artificial Intelligence (AI) emerges as a transformative force poised to revolutionise drug discovery, development, and patient care.

Status of Indian pharma sector:

• The Indian pharmaceutical industry is a traditional hub for the production of generic medicines and low-cost vaccines.
• While India holds third position globally in volume terms of pharmaceutical production, it comes at a dismal 14th place globally when ranked in terms of the value of production.

Challenges faced by Indian Pharma sector:

• The limitations faced by the Indian pharma industry in making breakthroughs in new drug discovery is linked to the general lack of resources.
• Conducting research and developing new medicines requires very high investments.
• Most Indian companies struggle to progress a drug beyond Phase II clinical research.
• The industry has also to compete with drug majors from the United States and Europe.

New development in Pharmaceutical sector using AI:

• China, which is largely in a similar situation as India as far as new drug discovery goes, has managed to make a breakthrough using AI.
• Hong Kong-based biotech startup Insilico Medicine has created the first fully Gen AI drug to advance to human clinical trials.
• The  AI was leveraged throughout the preclinical drug discovery process to identify a molecule target, generate novel drug candidates, assessment of binding efficacy with the target, and prediction of clinical trial outcomes. 
• Massachusetts Institute of Technology researchers have unearthed a potent new antibiotic compound capable of treating infections caused by drug-resistant bacteria.
• It is the first new antibiotic discovery in the last 60 years.
• Facilitating the identification of this compound was a machine-learning algorithm, surpassing traditional experimental approaches by rapidly screening over a hundred million chemical compounds.
• Merck and Pfizer are among the other pharma majors actively leveraging Gen AI. 
• Merck uses its proprietary platform, AIDDISON to help in drug discovery.
• Pfizer now employs Gen AI-powered chatbots to deliver personalised messages to clinical trial participants.
• In India, too, biotech incubators and pharma start-ups have started to experiment with Gen AI applications in highly targeted therapies. 
• The process, though, needs policy support, given that in India, the links between industry and academia are tenuous compared to the Western markets.

Available AI tools for Pharmaceutical Research and Development:

• AlphaFold Protein Structure Database (Google):
• • AlphaFold is an AI system developed by Google DeepMind that predicts a protein’s 3D structure from its amino acid sequence. 
  • AlphaFold Database provides open access to over 200 million protein structure predictions to accelerate scientific research.
    • A protein’s function is determined by how its constituent amino acid chains fold up into three-dimensional shapes.
    • Knowing a protein’s structure can help potentially alter its behaviour by introducing a drug that binds to the protein. 
    • The traditional methods for determining a protein’s structure have limitations.
      • They are slow, capital intensive, and complicated.
      • The researchers have only mapped the structure of less than 20% of the human body’s 20,000 proteins till end-2022. 
  • AlphaFold promises to solve this “protein folding problem”, but predicting the points where folds would be optimum.
• BioNeMo tool from Nvidia:
• BioNeMo is a generative AI platform that provides services to pharma companies to develop, customise and deploy foundation models for drug discovery.
• It enables scientists to integrate generative AI to reduce experiments and, in some cases, replace them altogether.

Significance of AI in Pharmaceutical and Healthcare:

• Drug Discovery and Development:
  • AI's predictive algorithms can sift through colossal datasets, identifying potential drug candidates and simulating their behaviour.
  • By significantly expediting the screening process, AI reduces both the time and resources required for drug development.
• Personalised Medicine:
  • AI's ability to process and analyse individual patient data allows for the customisation of treatment plans.
  • By considering genetic variations, lifestyle factors, and medical history, AI tailors therapies to maximise effectiveness and minimise side effects.
  • This marks a paradigm shift towards patient-centric care, where treatments are precisely calibrated to each individual's unique physiological makeup.
• Clinical Trials Optimisation:
  • By identifying suitable candidates based on diverse criteria, predicting patient responses, and streamlining the recruitment process, AI significantly accelerates the trial phase.
  • This leads to faster, more efficient trials with larger and more representative participant populations.
• Predictive Analytics for Patient Outcomes:
  • AI's ability to process vast amounts of patient data enables the prediction of disease progression, treatment response, and potential complications.
  • By harnessing these insights, healthcare providers can proactively adjust treatment plans, improving patient outcomes and reducing the likelihood of adverse events.
• Supply Chain and Operations Optimisation:
  • By forecasting demand, automating inventory management, and streamlining production schedules, AI minimises waste, reduces costs, and ensures timely delivery of pharmaceutical products.
• Regulatory Compliance and Quality Assurance:
  • AI-driven systems can meticulously monitor and analyze processes to ensure adherence to regulatory standards and product quality.
  • By automating compliance checks and conducting real-time quality assessments, AI mitigates the risk of non-compliance and costly recalls.
• Drug Repurposing:
• AI's analytical capabilities enable the exploration of existing drugs for new therapeutic applications.
• By analysing their molecular structures and mechanisms of action, AI can identify potential candidates for repurposing in the treatment of different conditions.

Challenges Impeding adoption of AI:

• Data Quality and Availability:
  • The limited availability of comprehensive, standardised healthcare data in India poses a significant challenge.
  • The industry must focus on data quality enhancement and aggregation strategies to overcome this hurdle.
• Regulatory Compliance:
  • Stringent regulations govern drug development in India, demanding meticulous planning for AI integration. 
  • Ensuring that AI applications align with safety and efficacy standards is paramount.
• Ethical and Privacy Concerns:
  • Safeguarding patient data while leveraging AI for personalised healthcare is a multifaceted challenge.
  • Striking the right balance between innovation and protecting individual privacy requires meticulous attention to legal and ethical standards.
  • Implementing stringent data anonymization techniques and encryption protocols can help address these concerns.
• Cost of Implementation:
• The substantial investment required for AI infrastructure, software, and training can be prohibitive.
• This is particularly challenging for smaller pharmaceutical companies or startups with limited financial resources.
• Exploring cost-effective solutions, cloud-based platforms, and open-source AI tools can help mitigate this challenge.

Significance of AI for Indian pharmaceuticals sector:

• AI can help the pharma industry in scaling up the value chain, driving innovation, and fostering a resilient ecosystem.
• With its world-class capabilities in formulation development, India's R&D ecosystem, coupled with the strategic utilization of government schemes, could enable Indian players to transition from a volume-oriented approach to a  value-centric strategy.

Case studies from India:

Way Forward:

• Artificial Intelligence, particularly Generative AI, could be a transformative tool for pharma companies that promises to significantly impact drug development in an industry that has witnessed limited progress in recent decades.
• In the realm of patient care, it is significantly impacting the patient journey by empowering patients to better manage their conditions, help in making decisions and overall, a seamless patient-doctor interface, it is imperative to underscore the importance of data governance and management. 
• As we embrace these technological advancements, ensuring data privacy and compliance with regulatory requirements must remain a top priority.