Pharmaceutical research, particularly for drug development, is being revolutionized significantly through technological innovations like virtual simulations and digital twins. The basic definition for any digital twin can be a virtual structure that is modeled on the real-world counterpart with the same internal links and processes. There must also be a mechanism for continual data inputs and updates into the virtual system from the real one. Let us learn more about how it can transform drug development and delivery.
Omics data has proliferated swiftly these days, containing valuable inputs on biomolecular activities in cells. Drug development and delivery involves several aspects like physiology of patients, drug attributes, and delivery systems. Conventional methods are often based on the trial and error framework which may be lengthy and costly. The development of digital or virtual models which simulate drug behavioral attributes within the body and forecast safety and effectiveness can be the solution that pharmaceutical research needed all this while.
Digital twins have immense future potential since it can enhance both development and outcomes for patients. The global market for this technology is anyway forecasted to witness 58.9% of compound growth annually, touching $48.2 billion by the year 2026 as per MarketsandMarkets. Digital twins will lower the time-to-market for pharmaceutical companies while enhancing development alike. It will enable higher optimization of dosages and formations prior to clinical trials as well.
Digital twins may be developed with data from several sources including wearables, imaging technologies, electronic health records, and the like. They may help in simulating drug behavior in our bodies, enabling a better understanding of possible side effects. Pharmaceutical researchers can thus tweak the administration of drugs and their dosages likewise. It may enable patients to get more personalized and better treatments, while the industry saves resources and time on the identification of any issues/hurdles before they actually happen in the physical world.
To cite a few instances, AstraZeneca has already tied up with Insilico Medicine for leveraging digital twin technologies across its drug candidate identification and drug development procedures. Pfizer has entered into a similar partnership with BioLingus for digital twins to optimization biologics formulation and delivery. Roche has itself made an investment in Physiomics for tapping digital twins while Novartis is using this technology in partnership with Lattice AI. Even Sanofi has made an investment in a startup using digital twin technology, namely Owkin, for faster drug development and discovery alike.
Digital twins can be helpful in predicting drug distribution, absorption, metabolism, and even excretion. They may leverage patient-based data including physiology, genetics, lifestyle aspects, and more. Digital twins may help researchers find future issues and optimize their formulations accordingly. They may also help simulate drug delivery systems including nanocarriers or implantable devices, enabling optimization of drug dosages and release rates accordingly.
The benefits are already being harnessed by several players in the sector. Ansys has been collaborating with researchers in Oklahoma State for creating a digital twin to boost drug delivery. They are enabling this via simulated lung models. The collaboration has unearthed the fact how 20% of several drugs achieved their desired targets. Hence, the digital twins are empowering them to redesign everything from drug composition attributes to particle sizes, leading to 90% growth in delivery-related efficiency. Drug delivery robots are another area where digital twins may come in handy. Researchers can simulate robot behavior in the body, enabling better function and design optimization. It may enhance precision in drug delivery while reducing human error-related risks alongside.
In a nutshell, digital twins for drug development and delivery promise the following advantages for pharmaceutical research and manufacturing:
Some key challenges in the implantation of digital twins for drug delivery and development are the following:
These are some of the potential challenges that pharmaceutical companies may encounter in terms of harnessing the benefits offered by digital twins.
Virtual reality (VR) can be a major game-changer for drug design, delivery, and development. VR can help in visualizing molecule interaction while also simulating molecular dynamics. It may help in understanding binding processes better along with lowering costs of clinical trials. These otherwise happen due to potential candidates failing during these trials. VR and computational design can enable more functional and structured simulations before synthesis along with improved virtual distribution, absorption, excretion, metabolism, and pharmacokinetics, along with toxicity.
The virtual design can be implemented at the initial stages of drug discovery. ML (machine learning) and AI (artificial intelligence) will help in predicting functions and determining structural attributes. Simulation and machine learning will enable forecasting major drug attributes for effectiveness and safety alike. Quantitative systems pharmacology and quantitative systems toxicology are among modeling strategies that may help develop in-depth tissue and organ models for forecasting efficiency or safety biomarker changes. Drugs can be designed within virtual human or animal environments for targeting clinical or preclinical pharmacokinetic outcomes and this will completely transform the pharmaceutical sector in the future.
Higher computational power and better application of ML-based algorithms have opened up future possibilities for highly advanced digital twins. They will tap analytics, simulation, and other technologies for better predictions, formulations, and outcomes along with fast-tracking innovation considerably. It goes without saying that the future of pharmaceutical research and development looks bright with technological progress spurring better outcomes for all industry stakeholders.
How can digital twins aid in the identification and prediction of potential off-target effects and safety concerns during drug development?
Digital twins can help immensely in identifying/forecasting possible side effects and safety issues at the drug development stage. They replicate the real world and hence researchers can use them to understand potential effects of drugs in the human body (simulated).
What are the limitations of digital twins?
Some of the limitations of digital twins include inaccurate or poor data quality and high computational power that is required for their effectiveness. If the data fed into the system is not accurate or high-quality, then the model will be impacted negatively.
Can big data replace traditional clinical trial methods?
Big data may eventually transform methods for clinical trials. It may enable improved predictive modeling of various biological processes and also drug development based on clinical and molecular data. It may also ensure better recruitment rates with patients chosen from numerous data sources. Big data may also help in lowering costs while making it easier to gather data alongside.
Can virtual simulations be used to predict drug interaction and side effects?
Virtual simulations using digital twins may be helpful for pharmaceutical companies to understand the effects of drugs in the human body. They can predict future side effects and drug interactions and tailor their dosages and formulations accordingly.
How will these technologies continue to evolve and improve?
Digital twin technologies will further evolve in the pharmaceutical space. They will enable models that simulate how diseases work in the body and dosages that work in combating them. They will integrate more seamlessly with AI and ML among other technologies to enable swifter insights and improved outcomes for the industry too.
