An era of unprecedented change is upon the pharmaceutical sector. Artificial intelligence is now deeply embedded in several pharma processes, from clinical research, drug discovery, and patient care to pharmaceutical manufacturing, as well as in laboratories conducting new experiments.
Rising costs of research and development in pharmaceutical manufacturing have led to delays in drug approvals, unproductive trials and an ever-growing exponential need for tailored drugs. However, with the introduction of AI and patient health data generation technologies, pharmaceutical companies are able to gain insight into tremendous volumes of information in no time, allowing them to abandon previously established means of doing business.
Innovative technology is used in the process of pharmaceutical manufacturing in order to improve industry efficiency and reduce product manufacturing costs. This technology can be different – from the use of artificial intelligence in drug discovery and development to the use of predictive technology in clinical trials.
Here we take a look at the ways AI and patient-generated health data stimulate changes in the pharmaceutical industry and what influence it has on the future of drug research and development.
Drug Discovery and Development
Traditionally, creating new drugs was a lengthy and costly endeavour. Sometimes it takes even more than 10 years and costs billions of dollars to introduce new medication or therapy to the market. But with the advent of AI, researchers can analyse large amounts of data about chemical compounds, biological relationships, and causes of diseases in a short time.
Machine learning analytics can determine how a compound can affect a specific biomolecular target, which allows organizations to cut down on time-consuming studies that would otherwise be required. At the same time, AI can find undiscovered patterns in genomic data by analysing large amounts of data quickly. As a result, new medications can be developed with higher molecular specificity.
Real-World Data (RWD) Harmonisation
Pharmaceutical research involves collecting information from various sources such as clinical histories, electronic health records (EHR), and patient-reported data sources. However, these data sources often contain unstructured or inconsistent data, which complicates analysis. The Patient-Generated Health Data (PGHD) approach offers a way to incorporate health information generated by patients into pharmaceutical research, providing additional insights into their health status and treatment experiences.
Platforms such as Carenity play a key role in collecting patient-generated health data (PGHD) by gathering patients’ self-reported experiences, treatment outcomes, and insights into living with chronic conditions. These data provide pharmaceutical researchers with valuable real-world perspectives that complement traditional clinical data.
Natural language processing and machine learning techniques can help in cleaning, organising, and harmonising these datasets to make them more manageable for the analysis. Accordingly, the pharmaceutical research teams can use the insights acquired from the analysis of these datasets to measure treatment adherence, look at real-world effectiveness, and evaluate the reasons leading to treatment discontinuation by the patients. Making the real-world data accessible and consistent would enable AI to help researchers better understand how treatments work outside the controlled clinical environment.
Continuous Patient Journey Analysis
AI gives researchers the ability to analyse flows of health data collected through the use of wearable technology, mobile health apps, or symptom-tracking programs. Through this data, researchers can gain insights and understand how the patients’ health has changed over time.
AI offers healthcare professionals opportunities to detect patterns of different disease types, find possible signals of deterioration in health conditions, and discover the way patients respond to therapies.
Continuous monitoring of patients can help researchers detect unexpected changes in the medical condition of patients that are usually missed between doctor visits. This way, healthcare specialists will be able to intervene in a timely manner and learn more about the treatment process.
Enhanced Research and Clinical Trial Efficiency
Artificial Intelligence can make a difference even in the notoriously long and inefficient process of clinical trials. Given that patients are the backbone of clinical trials, AI has a significant role in patient recruitment by enabling much faster detection of candidates than humans could achieve. This is critical for making sure that a sufficient number of participants of different backgrounds take part in clinical studies and reach meaningful conclusions.
Forecasting health analytics improve clinical trials even more since they allow working with the previously obtained data about clinical studies, real-world evidence, and patient health trends so that the treatment outcome can be predicted and possible troubles with safety can be revealed. Researchers can leverage this information to implement significant improvements in process efficacy and reduce possible costs.
Personalised and Precision Medicine
Every patient responds differently to treatment. Factors such as genetics, lifestyle, environmental exposures, and existing health conditions can influence how a medication works and the likelihood of adverse reactions.
AI technologies can evaluate the above factors together with data on the treatment-related activities of patients, allowing researchers to spot some trends in their behaviour. Connecting the data on the patients’ genetic makeup with their physiological characteristics and important health records enables researchers to analyse which treatments would be appropriate for which patient group. These results help with the creation of personalised and precision medicine, which aims to offer patients treatment that fits them better.
Moreover, AI is capable of predicting which patients are more likely to suffer serious side effects of treatment. However, those predictions can only be trusted when they have been validated in the clinical setting.
Accelerating Drug Repurposing
Another area where AI has made a significant impact is helping in reusing existing medications. Drugs in use today can be used for other ailments or medical issues. Taking aspirin as an example, it was originally used to relieve pain and reduce inflammation, but its ability to prevent blood clot formation was discovered later. Today, it is used in certain patients to reduce the risk of cardiovascular events. Similarly, Ozempic was developed for treating type 2 diabetes, and now its application has shifted to weight loss.
There are instances of lucky coincidences that lead to amazing discoveries. However, nowadays, thanks to AI technologies, all these discoveries have become more purposeful. AI is able to analyse existing medicines, looking for new uses. This allows the AI to combine information about drugs and new diseases’ prevalence and biological mechanisms. Thus, it allows for quicker delivery to the patient, lower risk of failure in development, and discovery of patterns that would probably be missed without AI.
Final Thoughts
The pharmaceutical sector is seeing unprecedented rapid transformation due to artificial intelligence. AI in conjunction patient-generated health information have become essential aspects for pharmaceutical companies, rather than trendy discoveries.
AI is making great changes and improvements in the drug industry, as it is used not only in the processes of drug development and creation but also in predictive analytics, smart manufacturing, personalized treatment, and other areas of medicine.
Ultimately, pharmaceutical companies that are fast in adopting innovations in the sphere of medicine will become leaders of the industry and will be among those responsible for implementing automated solutions and smart technologies in healthcare.
