Drug development and compliance: 4 Ways AI is impacting pharma
The pharmaceutical industry has long been reliant on rigorous research and development processes to bring life-saving drugs to market. However, the traditional methods of drug development are often time-consuming, expensive, and prone to human error. Artificial intelligence (AI) is changing that, bringing new opportunities for innovation and efficiency to the forefront.
Some of these areas of particular importance to pharma include helping accelerate drug discovery, clinical trials, and more personalised medicine, all while meeting stringent regulatory compliance requirements. This article will explore these four areas and how AI is impacting them, and will continue to do so for the foreseeable future.
1. Accelerating drug discovery
AI algorithms can analyse vast amounts of biological data, including genetic information, protein structures, and molecular interactions to identify potential drug candidates with greater speed and accuracy than ever before. By streamlining the initial stages of drug discovery, AI enables researchers to focus their efforts on the most promising leads, reducing the time and cost once associated with bringing new drugs to market.
AI-powered virtual screening techniques can also simulate the interaction between potential drug molecules and their biological targets, helping researchers identify compounds with the highest likelihood of success. This approach not only expedites the discovery of new drugs, but also increases the probability of finding compounds with desirable therapeutic effects and minimal side effects - something critically important when dealing with real patients.
2. Enhancing clinical trials
Clinical trials play a crucial role in evaluating the safety and efficacy of drugs before they’re approved for widespread use. But, historically, clinical trial methodologies are often inefficient and costly, leading to delays in the development process to the tune of months to years in some cases. Often, this is due to the human component of finding viable clinical trial candidates. AI provides a more accurate and timely process for patient recruitment, trial design optimisation, and data analysis.
AI algorithms can analyse patient data from electronic health records (EHRs), medical imaging, and even wearable devices to identify suitable candidates. This can aid researchers in predicting patient outcomes and optimising trial protocols to enhance the efficiency and reliability of clinical studies. Additionally, data analysis tools can then help researchers extract valuable insights from complex clinical datasets, accelerating the identification of potential adverse drug reactions and safety concerns, addressing them before any harm is done.
3. Personalised medicine and treatment adherence
As the name implies, personalised medicine involves tailoring medical treatment to an individual patient, rather than a larger group, like male or female, or a subset of patients, such as a male with type 2 diabetes. With good reason, personalised medicine is gaining momentum beyond personal devices, like Apple Watches and Oura Rings. AI plays an important role in enabling personalised medicine by analysing patient data, including genetic information, biomarkers, and social determinants of health to predict treatment responses and optimise therapeutic outcomes.
AI-powered digital health technologies, such as medical-grade mobile apps and wearable devices, are transforming the way patients manage their health and adhere to treatment plans. These technologies can provide personalised reminders, education, and support to help patients adhere to fitness goals, as well as their prescribed medications, ultimately improving treatment outcomes and reducing healthcare costs in the long run.
4. Improving regulatory compliance
None of the aforementioned areas would benefit from AI without strong regulatory guardrails in place. Pharma companies must adhere to strict regulations set forth by authorities such as the FDA and EMA to ensure their products are safe and effective. AI can go a long way in facilitating regulatory compliance by automating compliance processes, detecting potential risks, and ensuring data integrity throughout the drug development lifecycle and beyond.
In fact, there is AI software dedicated specifically to analysing regulatory requirements and helping organisations navigate complex and constantly changing compliance frameworks. By automating documentation, reporting, and quality control processes, AI reduces the risk of human error and ensures consistency in regulatory submissions and audits. That said, AI is not a fail-safe solution. With data privacy and high-stakes consequences top of mind, human oversight will be critical for responsible AI use - at least for now.
The impact of AI on drug development and compliance is already making waves, and it’s not slowing down. In fact, pharma companies that don’t embrace AI will struggle to remain competitive and dynamic as the technology matures. More importantly, the ethical and strategic use of AI will enable pharma companies to bring safer, more effective drugs to market at a faster rate, improving patient outcomes significantly.
