Digital Twins: A Way Forward To Unlock the Pivotal Hidden Signals To Increase ROI in Drug Discovery?
Parexel International
Singapore
“Digital Twin” is a virtual or digital copy of a real-world product or living entity (at the atomic or macro geometric level), developed by integrating virtual blueprints with real world data, and optimized through Artificial Intelligence and Advanced Analytics software. The “Digital Twin” concept was introduced by Michael Grieves in 2003. Currently, Digital Twins are implemented in various manufacturing industries, predominantly in aerospace, automobiles, and space technology.
This article evaluates the lack of robustness in current drug development processes, from pre-clinical to late phase, and how Digital Twin models can bring sophistication to unlocking the hidden signals and to increase Return on Investment (ROI) in pharmaceutical R&D.
Primary Cause of Late-Stage Clinical Trials Failure
- Beneficial effects seen in animal models were not replicated in humans,
- Poor understanding of disease,
- Poor definition of patient populations between phase 2 and 3,
- Phase 2 endpoints were not confirmed by phase III clinical outcomes,
- Insufficient sample size,
- Inadequate dosing in phase 2 and poor therapeutic indices,
- Phase 2 false positive effects were not replicated in phase 3,
- Data-related issues and unintentional unblinding,
- GCP violations,
- Recruitment drop-out and noncompliance with protocol, and
- Insufficient landscape assessment of current standard of care and precedents.
Digital Models and Mathematical Models
Currently available mathematical models lack details and are not powerful enough to predict the specificity and sensitivity of drug molecules over individual tumor components, including blood vessels and cellular components, such as biochemical pathways transmitting growth factor signals. Let’s take a brain tumor as an example and evaluate the possible advantage of Digital Twin models: although there are numerous small-molecule kinase inhibitors whose primary targets are of interest for neuro-oncology (e.g., epidermal growth factor receptors, vascular endothelial growth factor receptor inhibitors), there is a no approved drug for such inhibitors to treat brain tumors. This is primarily due to the Blood Brain Barrier (BBB) that prevents certain drug molecules from crossing into the brain and reaching target tumors.
Constructing a Digital Twin model of the BBB based on research data and the BBB properties (such as membrane structure, permeability, lipid and ionic structure, protein folding, three-dimensional structure of receptor drug targets) can overcome this obstacle. A Digital Twin BBB could be evaluated virtually to understand the channels that facilitate diffusion of small-molecule kinases to reach targets, such as the channels’ structure and ionic properties, factors that affect membrane permeability, and the proper molecule orientation. Based on this virtual model, a drug’s target sites could then be modified to allow crossing the BBB effectively and reaching targets.
Digital Twin Models for Preclinical Testing
Digital Twins for Phase 2 to Late Phase
Digital Twins of Hearts Could Help Diagnose and Treat Cardiac Disease
Creating data-driven Digital Twins of the vaccines with real-time data (using, for example, multivalent epitope structure, surface protein structure, and cell receptors, etc.) and of the clinical trial subject (considering the subject’s genetic profile and immunological response, such as antibody type, cytokines, interleukin response, antigen and antibody complex formation, and its impact at cellular level etc.) can provide visualized insights into antigens and their effect on immune cells. This in turn helps us better understand the scientific correlation between biomarkers and the corresponding immunological and clinical outcomes and ultimately reduce false-positive errors in Clinical Trials.
Potential false negative clinical outcomes could be avoided by understanding and developing digital models from data obtained from measuring biomarkers and from clinical pathways. For example, in a few instances, the biomarker pathway will have no impact, while the intervention affects the overall clinical pathway and improves clinical outcome.