Artificial Intelligence and Deep/Machine Learning(AI & D/ML) or Artificial Neural Network (ANN): Implications for pharmaceutical product development processes
Producing pharmaceutical products without untoward effects but with desired qualities is not only the very basic requirement set by regulatory authorities but also indirectly decides the success of the pharma industry. The pharmaceutical product development process is a combined and coordinated work from numerous divisions of the pharma industry which usually starts from API discovery, synthesis and utilization to ends with formulation development, market positioning and successful use at the consumer end.
Conventional API discovery and development process, as well as optimization of analytical method and formula of dosage form, routinely use the quality by testing (QbT) approach or one factor at a time (OFAT) strategy. Since the time-consuming and chemical wastage in OFAT strategy or QbT approach is inevitable, looking for a better alternative approach or strategy which shows less time consumption in conjunction with minimal chemical wastage becomes an urgent requirement. In terms of the response surface methodology (RSM) approach, the design of experiment (DoE) is being utilized effectively to optimize APIs synthetic process, analytical method development and formula optimization for final pharmaceutical products. It should be added that the RSM-linked DoEis based on a linear model and therefore it won’t consider the non-linear modelization concept to see the influence of independent factors on the response variables. Thus, depending solely on the DoEfor such optimization processes may end with an erroneous conclusion and thus necessitates applying another approach (preferably of non-linear model-based) which judiciously eliminates the conclusion errors noticed with the DoE-based optimization process. One such non-linear model-based approach recently introduced in the pharmaceutical product development process is artificial intelligence and deep/machine learning (AI & D/ML) or artificial neural network (ANN). Non-linearity refers to a massive parallel network distributed throughout that allows for approximation and real-time operation to exhibit unpredictability and random behavior. The information processing capacity of ANN is related to the functioning of the normal human brain. Figure 1 shows the possible areas of pharmacy discipline wherein the AI & D/ML or ANN can be integrated.
This article briefly discusses the financial dealings of pharma companies related to the application of AI & D/ML or ANNin drug discovery and development processes as well as for optimization of analytical method conditions and formula of the dosage form.
Pharma companies’ financial dealings
Table 1 displays the financial dealings of Pharma companies concerning the application part of AI & D/ML or ANN, particularly in drug discovery and development processes. The entry of AI & D/ML or ANN helps to shorten not only the new drug development period but also significantly minimizes the utilization of manpower and considerable reduction in expenditure related to the API development. For example, the German-based biotechnology company, Evotec, has partnered with a UK-based company, Exscientia, for the small molecule drug discovery process. Within a short period of 8 months, the discovered small drug molecule entered Phase 1 clinical trials which might usually have taken 4-5 years to deliver the drug candidate from the traditional drug discovery process (without utilizing AI & D/ML or ANN).
Comments
0 comment