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The cost of developing a new drug roughly doubles every nine years (inflation-adjusted) aka Eroom’s law.
As the volume of data for drug discovery and development explodes, the ability of Pharma and medical practitioners to keep up and accelerate drug discovery becomes more challenging. In contrast, the software industry reaps the benefits of Moore’s law with high powered computing, and increased internet bandwidth.
How can Pharma transition from Eroom’s law to Moore’s law and beyond?
A transition to Moore’s Law can only be achieved by the joint effect of technological and conceptual paradigm shifts. In the past few years, AI has emerged as a promising technological solution to address many of the problems plaguing the industry.
At Innoplexus, we develop AI technology for the pharmaceutical industry to unleash the value of machine learning and solve many of the challenges facing Pharma such as high costs in R&D, long timelines, and inadequate research. We have made substantial progress in generating intelligence and insights across the pre-clinical, clinical, regulatory, and commercial phases of drug development to discover the most relevant knowledge and patterns.
As Pharma companies begin to leverage AI, applications of machine learning will improve Pharma processes. A few examples are as follows:
Drug Discovery – Combining the disease expertise of pharmaceutical companies with advanced applications of machine learning models yields unprecedented efficiencies in drug discovery. The collaboration between Pharma and IT companies enables the preparation of large and diverse datasets to build ML models that accelerate drug discovery and development. Today, Pharma is adapting new AI technologies and tools to leverage their capabilities more than ever before.
Biomarker Development – AI technology is adopted by several pharmaceutical companies to advance diagnostics, toxicology, and biomarker discovery in the areas of neurology, endocrinology, and oncology. For example, machine learning identifies the molecular signatures and potential targets for precision medicine.
Clinical Trials – Machine learning can improve the outcome of clinical trials by examining hundreds of parameters and possible values to predict which ones need to be altered to increase the probability of success. For instance, by comparing clinical trials held in the past for similar diseases or drugs, machine learning can help determine the optimal sample size, reduce errors, and adjust protocols at different geographies. Machine learning based solutions improve these studies by connecting drugs, indications, study design, trial information, patients, authors, and sponsors with real-world events.
When Machine Learning is adopted early in the drug development cycle, it can reduce the drug discovery time, streamline the research process, accelerate development, and create opportunities to repurpose drugs and diversify pipelines. According to a Deloitte1 study, in the next five to ten years, the number of companies to adopt AI technologies for drug discovery will increase exponentially and research will be done mostly in silico.
Embracing AI and machine learning will help address the challenges of increasing costs and declining ROI for pharmaceutical R&D.