By Jack Hidary (Chief Executive Officer, SandboxAQ)
Despite advances in biotechnology, there are still more than 7,000 diseases today with no effective treatment. There also continue to be disparities in global health as treatments for many infectious diseases do not attract sufficient research funding and pharmaceutical attention. According to the World Health Organization, nearly 2 billion people – one-quarter of the world – have no access to essential medicines.
Artificial intelligence (AI) and “simulation” are two technologies that promise to redefine drug development and enhance our ability to respond to global health crises.
The journey from molecule to medicine for a new pharmaceutical drug is long, laborious and expensive. Developing a single drug can cost between $1.3 billion to $4 billion and take between 10 and 15 years. Thousands of promising compounds are conceived in laboratories each year but only a fraction survives the arduous journey to clinical trials, where success is still not guaranteed – in the United States, roughly 88% of drugs that reach clinical trials fail to make it through and gain approval.
Successful drug development requires navigating a labyrinth of molecular combinations – identifying the most promising compounds that deliver the intended effect while avoiding unforeseen toxicities and adverse side effects. With enough data and computing power, AI can help researchers identify and mitigate these issues, creating safer, more effective treatments and accelerating time to market. In some cases, however, there is little or no data or analysis available upon which to formulate effective drug compounds, particularly for cancer, Alzheimer’s and other previously undruggable conditions.
The difficulty, expense and low success rates of new drug development greatly influence which disease programs are pursued, discouraging therapies for less common or more challenging diseases and those disproportionately affecting poorer populations.
Breaking through the challenges posed by previously undruggable targets requires technology that moves beyond AI into the world of first-principles physics. When we don’t have the data for AI to design effective therapeutics, simulation can fill the void, creating new datasets by digitally modelling biopharma compounds and rapidly simulating their chemical interactions with molecular targets within the human body.
Simulating these digital twins requires advanced computing techniques – driven today by classical graphics processing unit (GPU) hardware and in the future by hybrid GPU-quantum chipsets to solve the quantum-mechanical equations that govern electron-level molecular interactions. Recent GPU performance acceleration has enabled these simulations to run at relevant scales, unlocking simulation’s potential in previously unavailable ways.
Approaches that use AI and quantum physics for scientific experimentation through computers unveil answers to questions previously answerable only through lengthy and costly physical experimentation. The insights gleaned from observing chemical interactions at the molecular level generate the data that researchers need to develop promising new treatments for the most challenging-to-treat illnesses.
“In the United States, roughly 88% of drugs that reach clinical trials fail to make it through and gain approval.”
— Jack Hidary, Chief Executive Officer, SandboxAQ
The impact of advanced computing technologies extends beyond drug development. Quantum sensors, with their inherent sensitivity to their surrounding environment, promise precise medical imaging and diagnostics, measuring even the most subtle deviations in magnetic fields surrounding vital organs.
When coupled with AI to eliminate background noise, these sensors unlock medical insights and images that were previously unattainable bedside. Work is being done to make these devices more powerful, portable and cost-effective so that they can be used in rural, remote or military hospitals, ambulances and other locations where traditional diagnostic imaging equipment is unavailable, unusable or cost-prohibitive.
Predictive capabilities can also deliver other healthcare-related insights and optimize global supply chains and logistics. With sufficient data, we have the potential to address public health threats. We can enhance our capacity to forecast spikes in demand for essential medical resources, ingredients and supplies, streamlining their delivery to areas where they are most urgently required.
Moreover, as cyberattacks continue to rise, particularly against healthcare organizations, implementing modern cryptography management with post-quantum cryptography standards can secure organizations against current and evolving threats. Protecting intellectual property and other sensitive healthcare data could reduce research and development (R&D) costs and potentially impact affordability.
The COVID-19 pandemic tested our global healthcare system’s capabilities, uncovering many shortcomings and highlighting its potential for improvement. Among them was the need for greater international collaboration between the academic communities and the technology, pharmaceutical and government sectors to develop new processes and technologies to prevent future pandemics and improve the delivery of care.
On the technology front, QuPharm, a consortium of 17 companies, has joined with the Pistoia Alliance, the Quantum Economic Development Consortium and Innovate UK’s Industrial Strategy Challenge Fund to explore AI and quantum technologies’ applications in healthcare, with the stated aim of building a quantum computer to “overcome common obstacles to innovation and to transform R&D.”
As the World Economic Forum’s National Quantum Blueprint details, such collaboration is essential for fully realizing the profound impact of these highly specialized and resource-intensive technologies and to underscore the importance of global, cross-disciplinary partnerships.
AI and other advanced computing paradigms like simulation promise to revolutionize patient care, enabling the rapid creation of new drugs and ensuring their availability when and where they are needed most. It can empower the next generation of scientists to explore new therapeutical frontiers, leverage enhanced medical imaging and cybersecurity, and prepare the world for unforeseen global health challenges.
The promise of AI and simulation is boundless but we must work together, bridge gaps and embrace a future where healthcare is more accessible, efficient and effective. Let us embark on this transformative journey together to better healthcare, society and humanity.
