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Over the past eight months, the COVID-19 pandemic has shed increasing awareness on the importance of the biotechnology sector and the pivotal role it plays in ushering new discoveries in disease research, treatments, and cures. While indeed, the notion of biotechnology’s importance in both the global economy and human health has never been more apparent to the keen observer, one of the most fascinating elements has been the rate at which novel discoveries are being developed. The traditional biotech research and development (R&D) process has long been marred by bottlenecks and redundancies spanning data collection, recording, analysis, and extrapolation as well as wet-lab benchwork. However, over the past decade, a powerful yet subtle tangential shift in how scientific data is harnessed, processed, and translated into functional insights has occurred. In the healthcare and life sciences space, there has been an unfathomable amount of data created and procured from high-throughput technologies in healthcare institutions and research labs worldwide. With these vast troves of data, only a finite amount has been cleaned, labeled, and structured to generate functional clinical and scientific insights to improve current standards of clinical care and develop novel discoveries.
Traditional data analysis methods in biotechnology have been relatively primitive, in which these methods were typically only functionally compatible with simple, homogenous data. However, these conventional methods begin to fail when the data becomes multivariate and heterogeneous. Often, data such as electronic health records (EHRs) include multiple variables, including diagnoses and comorbidities for patients. This is true for drug development and discovery as well as clinical trials, in which utilizing heterogeneous human data is vital for developing more efficacious drugs and trial protocols. AI can analyze and integrate complex, heterogeneous information in order to create stratified genotypic and phenotypic patient groups. This holds immense potential in revolutionizing clinical trial design, drug development, precision medicine, and a host of other biotechnology processes, which we further discuss below.
Like many other game-changing technologies in biotechnology such as CRISPR/Cas9 gene editing, computed axial tomography in 3-dimensional bio-fabrication, or RNA interference (RNAi), the symbiotic integration of artificial intelligence (AI) with biotechnology has generated immense interest by members of the scientific community. AI applications in biotechnology span a plethora of unique application domains, including structural genomics, proteomics, pharmacogenetics, drug synthesis and engineering, target identification, and predictive modeling. Besides, AI algorithms have been deployed for managing clinical trial data, protocol development, and patient enrollment, as well as laboratory and workflow process automation. These process applications have dramatically improved the cost-benefit dynamics within biotechnology processes, otherwise denoted as “bioprocesses.” Specifically, AI can significantly reduce costs ten-fold while simultaneously enhancing the functional utility and value generation of bioprocess outputs. For example, computational approaches in drug synthesis and design have led to a thousand-fold increase in hit-to-lead rates, an order of magnitude that would have been thought to be unfathomable only 15 years ago. This process ultimately reduces the cost and failure rate of assets, representing a shift in the traditional drug discovery model that has been plagued by high rates of failure and unsustainable costs. Furthermore, the applications of AI in leading a new era of precision medicine are promising. The ability to utilize oncological genomic data and real-world evidence (RWE) from cancer patients and deploy structured AI algorithms to match them to more efficacious chemotherapies represents an exciting yet practical use case for improving current standards of care.
While the convergence of artificial intelligence and biotechnology is an exciting symbiosis, there is still much work to be done. The boom in AI startups has led investors to be more scrupulous in defining the real-world functional utility of AI in creating realized actual value for customers and institutions. An AI algorithm is only as good as the data that it is trained on; thus, garnering access to high-quality, structured, cleaned, and labeled data represents a significant bottleneck for the integration of AI. The future is, however, very bright for AI-biotechnology companies. The ability to utilize human data and ex-vivo processes for therapeutic testing, screening, development, and deployment will accelerate novel treatments and cures to market faster with the promise to dramatically improve the current standard of human health globally.