There are several factors that contribute to the growing confluence of Artificial Intelligence (AI) in the pharmaceutical industry. The increased rate of expansion in the types of new chemicals being manufactured alongside the increased complexity of chemicals fosters digitalisation of health records.
This digitalisation calls for the pharmaceutical experts and industrialists to use the technology of machine learning and AI to find applications. A great variety of biomedical datasets are also urging pharmaceutical professionals to make use of upcoming technology to make business more efficient and effective.
One of the distinctive areas where AI finds applications in the pharma industry is in the process of the formulation of medicines. Technology finds applications in deciding and conducting operations that process not only the ratios of the chemicals being used in the making of medicines but also the physical states in which they must be added. Although the correlation between the product performance, ingredient levels, and the processing conditions may not be thoroughly known, AI helps to find some patterns from preexisting results which can help decide which chemical is most suited in developing a broad-spectrum medicine.
AI in pharmaceutical operations
Previously, pharma formulators were using statistical techniques to optimise time taken in pharmaceutical product formulation. They were relying on the data generated by machinery such as titration machines, centrifuge machinery, mixers and tanks that have in-built sensors.
AI is also capable of predicting the response of a patient before they are subjected to a drug. It can help pharmacists understand the possible relationship among various factors that directly impact the results of a certain drug on potential patients. AI helps manufacturers avoid expensive procurement of chemicals that can lead to unexpected and unfavourable pathological results. Employing AI techniques can also help ensure beforehand that chemicals and compounds in the medicine possess applicable characteristics. Medicines are favoured if they can get actively absorbed in the human body (with minimal side effects) and are distributed around the body with uniformity without hindering any metabolic activities.
Recently, mathematical and IT advances have given rise to developments in alternative modeling as well as data mining techniques. These techniques work with a wider range of data sources known as neural networks. Neutral mining is a technique that mimics the process of how the human mind works. Pharmacists are increasingly trying to adopt neural networks in generating genetic algorithms (that revolve around mimicking evolutionary aspects of humans of self-adapting and organising abilities). Using neural networks, pharmacists can decipher ‘fuzzy logic’ in humans that allow patients to draw conclusions and generate responses which are based on incomplete and imprecise information. Scientists are still formulating, analysing and processing medicines based on their future scope of application with the help of innovative technology such as AI, machine learning as well as big data segmentation.
Practical applications of AI in the pharma industry
The average biometrical researcher operates with a plethora of information and data sets every day. AtomNet technology is one type of Neural Network that makes operational decisions on pharmaceutical coatings, the structure of the drug design and the superficial structural composition.
Using AI, pharmacists can virtually combine atoms and compounds which help scientists to foresee the course of operation of the drug when inside the human body. To some extent, this eliminates the need for the final phase trial for the drug that involves human volunteers ingesting the drug. Neural Networks also function by generating algorithms that are trained to analyse and process atoms and decide which ones will bond. Pharmaceutical product formulation additionally uses AI in the selection procedure for the patients going under trial. Through data mining, patients with the most favourable chance of yielding any adverse effects of the drug are selected. For instance, a patient of thyroid gland dysfunction is selected to take the drug test for a salt meant for regulating thyroxine hormone.
AI and machine learning can help the pharma industry build a strong and sustainable pipeline of generating new medicines. Augusta - a data processing and analytics application helps in the integration of healthcare ethos with the business model of the pharmaceutical company.
Developed countries such as the US, Australia, and Finland are employing AI and related technologies in developing drugs for ailments such as Parkinson’s and Alziemers Disease. Cardiovascular diseases resulting from carbon black poisoning is affecting a large population in developing countries and pharmaceutical companies in countries such as India, China, and Bangladesh are formulating medicines which are aimed at potential patients in these regions.
Pharmacists make use of AI, machine learning and big data analytics to simplify complex methodologies of formulating drugs by using simplified processing techniques. As AI moves from theoretical studies to practical applications, implementation of neural networks, fuzzy logic, and genetic algorithms are shaping how research governs the future of medicines across the globe.