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Daily Current Affairs for UPSC

AI in Drug Development

Syllabus: Science and Technology [GS Paper-3]

Context

Artificial intelligence (AI) is transforming various industries, and drug development is no exception. AI’s potential to streamline and accelerate the drug development process has made it an attractive tool for pharmaceutical companies. In this article, we will explore the use of AI in drug development, its benefits, and the challenges associated with its implementation.

AI in Drug Discovery

AI algorithms can do data crunching as AI can analyse pertinent data such as molecular structures, biological interactions and clinical trials outcomes. This thus lets researchers to detect the drug targets, observe the drug results and fine-tune the drug candidates. Fueled drug discovery platforms, for instance Watson for Drug Discovery from IBM, are being used by pharmaceutical technologies to precisely shorten the discovery process in recent times.

AI in Lead Optimization

  • Lead optimization stands for a critical development stage in drug therapy where researchers modify drug candidates to their effectiveness and safety parameters. AI algorithms can predict the pharmacokinetic and pharmacodynamic characteristics of potential drugs and these predictions can aid drug hunters to work more efficiently to select the most promising drug candidates.
  • AI-driven projects can manufacture a new drug era by using already available information on drugs and their results. They are able to enhance current drugs in order to achieve an optimum effect, minimise side risks and predict the interactions of different drugs.

AI in Clinical Trial Design

  • Artificial intelligence (AI) can be used in medical research to find the most efficient and effective clinical trials where new drugs can be tested biologically, faster and with good efficiency. 
  • Thus, AI can be used to identify suitable patient populations, predict drug responsiveness, and simplify the design of trials. Artificial intelligence (AI)-powered clinical trial platforms are now being utilised in pharmaceutical companies’ operations for the purpose of enhancing the success of trials on a large scale, with examples like Medidata’s AI-powered trial design.
  • AI is currently changing clinical trials by using the most correct patient selection, monitoring, and data analysis which leads to successful trials and patient centred treatment programs. it can determine how the patient will answer in the medicine proceedings, which assists in drawing up personalised strategies for medicines.

AI in Drug Safety and Toxicity Prediction

AI algorithms can predict drug safety and toxicity by analysing large datasets of drug interactions, adverse events, and toxicity profiles. This enables researchers to identify potential safety issues earlier in the development process, reducing the risk of late-stage drug failures.

AI in Personalized Medicine

AI can help personalise drug treatment by analysing individual patient data, including genetic profiles, medical histories, and treatment responses. AI-powered personalised medicine platforms, such as Foundation Medicine’s FoundationOne, are already being used by healthcare providers to optimise drug treatment for individual patients.

Challenges and Limitations

  • Data Quality and Quantity: The more data AI has, the faster and better it can learn. Machine learning algorithms require high volume, good quality data. In drug development, however, getting data of this kind is becoming increasingly difficult because of patients’ privacy concerns, massive data silos, and complex biological data.
  • Integration with Existing Workflows: Implementation of AI into the existing processes for finding drugs necessitates major adjustments for the progress workflows. However, it can be a time-consuming effort and might be a challenge to do this, as it involves training personnel, adapting protocols and guaranteeing that AI systems operate smoothly with existing ones.
  • Interpretability and Explainability: AI models that are frequently used like deep learning are mostly known as ‘black box’ because their decision-making processes are usually not transparent to most people. The interpretability deficiency or the barrier to trust and acceptance for the researchers and regulators is one of the characteristics that depicts the true nature of artificial intelligence.
  • Ethical and Legal Considerations: AI application may have ethical dilemmas such as arching to prejudice, overlook ability as well as the possibility of bad use. Legal dilemmas also exist in this context, for example, regarding intellectual property laws and data governance.
  • Technical Limitations: AI models can be restricted and not be able to solve a wide range of tasks by design and algorithms. They could be sometimes applicable to less complicated biological configurations or not capture all the elements of a chemical relationship in detail, hence being less accurate.
  • Cost of Implementation: Industries have to budget and design AI solutions can be costly. It is not only the price of the technology itself which matters but the cost of the infrastructure and expertise to run and maintain it effectively.

Conclusion

The use of AI in drug development has the potential to transform the pharmaceutical industry by accelerating the discovery and development of new drugs, improving drug safety and efficacy, and personalizing drug treatment for individual patients. While there are challenges and limitations associated with AI implementation, the benefits of AI in drug development make it an exciting and promising area of research and development.

Source: The Hindu

UPSC Mains Practice Question

Q. Discuss the role of artificial intelligence in revolutionizing drug development. What are the potential benefits and challenges associated with integrating AI into pharmaceutical research and development?

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