Avant View

How is AI reshaping the Life Sciences from Drug Discovery to Biomanufacturing and Beyond

Almost every industry today seeks to implement and benefit from the advances of artificial intelligence (AI) and machine learning. From reduced costs and time requirements to improved scalability and reproducibility, researchers and operators are just starting to reap the benefits from the enormous potential of AI. Specifically, in the rapidly evolving landscape of life sciences, AI and machine learning stand out as transformative forces driving innovations throughout the lifecycle starting upstream in drug discovery through to biomanufacturing and beyond to Healthtech with patient-touching implications. As a growth equity fund dedicated to therapeutic-enabling tools in the life sciences, Healthtech and TechBio, AVANT BIO is strategically investing in companies implementing AI across the convergence of technology, biology, and healthcare. Below we attempt to summarize just some ways we expect the life sciences industry to evolve due to the far-reaching impacts of AI and machine learning.  

Upstream: AI in Drug Discovery 

The integration of AI into drug discovery heralds a new era where speed, efficiency, and precision are paramount. AI is now an integral component for optimizing drug design and drug development, ensuring that promising compounds swiftly transition out of the lab and into clinical trials​. The most effective data-intensive platforms will leverage AI for improved speed and reduced failure rates for drug discovery.Specifically, some ways in which AI can be applied in the drug discovery workflow include: 

  1. Drug Target Identification: AI-enabled technologies are improving initial assessments of potential drug targets by over 30%, (McKinsey & Company), enhancing the speed and accuracy with which researchers can identify viable targets for new drugs. These technologies analyze vast databases of chemical compounds and biological data to predict interactions and functionality, thereby prioritizing which compounds to test further​. 
  2. In Silico Compound Screening: Generative AI models are employed to accelerate the screening of chemical compounds, significantly reducing the time needed to identify new leads from months to weeks. Such models can predict the structure and functionality of small molecules and amino acids allowing for more efficient exploration of chemical optionality​ and rapid assessment and prioritization of potential therapies.
  3. Optimizing Drug Design: AI is shaping the design of large molecules, such as proteins and mRNA, used in targeted therapies and vaccines. AI models predict the sub-structures of these molecules, facilitating the in-silico design of new drugs and enhancing the predictive accuracy of drug efficacy in discovery assays​. 
  4. Streamlining Drug Development: The integration of AI into lab operations is seen as a transformative step towards more automated labs. AI models assist researchers in designing and synthesizing molecules more effectively. Such automation of routine tasks in the lab allows for more focused and innovative research efforts​ (Drug Discovery and Development). 

Downstream: AI in Biomanufacturing 

AI in biomanufacturing is revolutionizing traditional bioprocessing methodologies and systems. From enhancing operational efficiencies and reducing waste to optimizing resource allocations, AI algorithms are uncovering insights throughout the bioproduction process. The industry is just starting to reap the benefits of AI applied to biomanufacturing which ultimately reduces cost burdens for patients and the broader healthcare ecosystem.  

  1. Digital Twins – Virtual Replicas for Real-Time Optimization: One of the most transformative applications of AI in biomanufacturing is the creation of digital twins (defined as a virtual replica of a physical process, product, or system). In biomanufacturing, digital twins allow scientists and engineers to simulate and then analyze the behavior of biological systems under various conditions without the need for physical experimentation. By using AI to create and refine digital twins, biomanufacturers can predict how changes in process parameters will affect key parameters like product quality and yield. 
  2. Design of Experiments (DOE): DOE is a systematic approach to process development that involves planning, conducting, analyzing, and interpreting controlled tests to evaluate the factors that may influence a process or product. AI enhances DOE by automating the analysis of large datasets, identifying the most critical variables, and predicting optimal conditions for process performance.  In biomanufacturing, AI-driven DOE allows for more efficient exploration of process parameters, leading to better understanding and control of biological processes, accelerating the development of robust, scalable processes that produce high-quality therapeutics.  
  3. Automating Biomanufacturing Processes: Automation and control are essential components of modern biomanufacturing for which AI can play a crucial role in advancing. AI-driven automation enables continuous monitoring and control of bioprocesses ensuring consistent product quality and reducing the risk of human error.  AI algorithms can also analyze data from sensors in real-time to detect deviations from optimal process conditions. If an anomaly is detected, an AI-enabled system can automatically adjust process parameters or alert resources to take corrective action. This level of control not only improves the efficiency and reliability of biomanufacturing processes but also enhances compliance with regulatory standards. Specifically, we have seen startup companies developing technologies that leverage AI-enabled automation in bioreactors, robotic manufacturing platforms, and downstream supply chain optimization. 

For the Patient: AI in Health Technologies 

The broader healthcare industry has long been plagued by inefficiencies where highly demanding resource and cost requirements across workflows results in exponential prices for developers and patients alike. AI-enabled technologies can improve end-to-end processes in the healthtech ecosystem via automation, data analysis, and robotics resulting in improved patient outcomes and experiences. Some specific examples:  

  1. Diagnostic Accuracy: Leveraging machine learning algorithms, AI can analyze vast amounts of medical data, including medical images, genetic information, and patient history to identify patterns that may be missed by human practitioners. For instance, AI-enabled tools and technologies enhance diagnostic accuracy and speed, enabling earlier detection of diseases with greater precision. Such early detection is crucial in improving patient outcomes as it allows for timely intervention.  AI can also play a significant role in reducing diagnostic errors by cross-referencing a patient’s symptoms and medical history with vast multimodal databases.  
  2. Streamlined Administrative Tasks: Healthcare systems are often hampered by the overwhelming volume of administrative tasks that consume valuable time and resources. AI can alleviate this burden by automating routine tasks such as patient scheduling, billing, and record management, enabling healthcare professionals to spend more time on patient care and improving healthcare outcomes. AI can also enhance the efficiency of medical record-keeping. Natural Language Processing (NLP) algorithms can extract relevant information from unstructured data in electronic health records (EHRs), ensuring that critical patient information is easily accessible and reducing the time clinicians spend on documentation.
  3. Improved Patient Care and Monitoring: AI’s ability to provide personalized care is a game-changer. By analyzing individual genetic information and medical history, AI can develop personalized treatment plans tailored to each patient’s unique needs. This approach, known as precision medicine, ensures that treatments are more effective and less likely to cause adverse reactions. AI also plays a crucial role in patient monitoring, particularly for chronic conditions. Wearable devices equipped with AI can continuously monitor patients’ vital signs and detect any abnormalities in real-time. 

Conclusion 

AI in life sciences is not just a trend – it is the cornerstone of future end-to-end advancements across the healthcare continuum.  In upstream drug discovery, AI is enabling more rapid development cycles, reduced costs, and potentially more effective therapies​. Further downstream,AI is revolutionizing biomanufacturing by reducing reliance on manual effort and incorporating automation contributing to more efficient processes and higher quality outcomes. Finally, the integration of AI in healthtech marks a significant step forward in the overall evolution of healthcare and the patient experience.  By investing in companies leveraging AI, AVANT BIO is part of this transformative movement that promises to bring forth new solutions to some of humanity’s most pressing health challenges. 

References 

“Generative AI in the pharmaceutical industry: Moving from hype to reality” – McKinsey and Company 

“AI and scientists take turns at the wheel of drug discovery” – Drug Discovery and Development 

“How AI is Transforming Biomanufacturing” – Bioprocess Online 

“The Role of Digital Twins in Biopharma” – Fierce Pharma 

“Biopharma doubles down on digital twins, AI” – Deloitte 

“Vineeta Agarwala on the promise – and limits – of AI in drug discovery” – McKinsey & Company   “AI Jobs to be Done in Life Sciences” – Andreessen Horowitz