By Ariana Tantillo
The framework could revolutionize drug design by supporting accurate and rapid calculations of how strongly compounds bind to target molecules
Solutions to many real-world scientific and engineering problems—from improving weather models and designing new energy materials to understanding how the universe formed—require applications that can scale to a very large size and high performance. Each year, through its International Scalable Computing Challenge (SCALE), the Institute of Electrical and Electronics Engineers (IEEE) recognizes a project that advances application development and supporting infrastructure to enable the large-scale, high-performance computing needed to solve such problems.
This year’s winner, “Enabling Trade-off Between Accuracy and Computational Cost: Adaptive Algorithms to Reduce Time to Clinical Insight,” is the result of a collaboration between chemists and computational and computer scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory, Rutgers University, and University College London. The team members were honored at the 18th IEEE/Association for Computing Machinery (ACM) International Symposium on Cluster, Cloud and Grid Computing held in Washington, DC, from May 1 to 4. “We developed a numerical computation methodology for accurately and rapidly evaluating the efficacy of different drug candidates,” said team member Shantenu Jha, Associate Professor at Rutgers ECE and Chair of the Center for Data Driven Discovery, part of Brookhaven Lab’s Computational Science Initiative. “Though we have not yet applied this methodology to design a new drug, we demonstrated that it could work at the large scales involved in the drug discovery process.”
Drug discovery is kind of like designing a key to fit a lock. In order for a drug to be effective at treating a particular disease, it must tightly bind to a molecule—usually a protein—that is associated with that disease. Only then can the drug activate or inhibit the function of the target molecule. Researchers may screen 10,000 or more molecular compounds before finding any that have the desired biological activity. But these “lead” compounds often lack the potency, selectivity, or stability needed to become a drug. By modifying the chemical structure of these leads, researchers can design compounds with the appropriate drug-like properties. The designed drug candidates then move along the development pipeline to the preclinical testing stage. Of these candidates, only a small fraction enters the clinical trial phase, and only one ends up becoming an approved drugfor patient use. Bringing a new drug to the market can take a decade or longer and cost billions of dollars.
Overcoming drug design bottlenecks through computational science
Recent advances in technology and knowledge have resulted in a new era of drug discovery—one that could significantly reduce the time and expense of the drug development process. Improvements in our understanding of the 3D crystal structures of biological molecules and increases in computing power are making it possible to use computational methods to predict drug-target interactions.
In particular, a computer simulation technique called molecular dynamics has shown promise in accurately predicting the strength with which drug molecules bind to their targets (binding affinity). Molecular dynamics simulates how atoms and molecules move as they interact in their environment. In the case of drug discovery, the simulations reveal how drug molecules interact with their target protein and change the protein’s conformation, or shape, which determines its function.
However, these prediction capabilities are not yet operating at a large-enough scale or fast-enough speed for pharmaceutical companies to adopt them in their development process. “Translating these advances in predictive accuracy to impact industrial decision making requires that on the order of 10,000 binding affinities are calculated as quickly as possible, without the loss of accuracy,” said Jha. “Producing timely insight demands a computational efficiency that is predicated on the development of new algorithms and scalable software systems, and the smart allocation of supercomputing resources.”
Jha and his collaborators at Rutgers University, where he is also a professor in the Electrical and Computer Engineering Department, and University College London designed a software framework to support the accurate and rapid calculation of binding affinities while optimizing the use of computational resources. This framework, called the High-Throughput Binding Affinity Calculator (HTBAC), builds upon the RADICAL-Cybertools project that Jha leads as principal investigator of Rutgers’ Research in Advanced Distributed Cyberinfrastructure and Applications Laboratory (RADICAL). The goal of RADICAL-Cybertools is to provide a suite of software building blocks to support the workflows of large-scale scientific applications on high-performance computing platforms, which aggregate computing power to solve large computational problems that would otherwise be unsolvable because of the time required. In computer science, workflows refer to a series of processing steps necessary to complete a task or solve a problem. Especially for scientific workflows, it is important that the workflows are flexible so that they can dynamically adapt during runtime to provide the most accurate results while making efficient use of the available computing time. Such adaptive workflows are ideal for drug discovery because only the drugs with high binding affinities should be further evaluated.
Jha’s team demonstrated how HTBAC could provide insight from drug candidate data on a short timescale by reproducing results from a collaborative study between University College London and the London-based pharmaceutical company GlaxoSmithKline to discover drug compounds that bind to the BRD4 protein. Known to play a key role in driving cancer and inflammatory diseases, the BRD4 protein is a major target of bromodomain-containing (BRD) inhibitors, a class of pharmaceutical drugs currently being evaluated in clinical trials. The researchers involved in this collaborative study are focusing on identifying promising new drugs to treat breast cancer while developing an understanding of why certain drugs fail in the presence of breast cancer gene mutations.
HTBAC not only has the potential to improve the speed and accuracy of drug discovery in the pharmaceutical industry but also to improve individual patient outcomes in clinical settings. Using target proteins based on a patient’s genetic sequence, HTBAC could predict a patient’s response to different drug treatments. This personalized assessment could replace the traditional one-size-fits-all approach to medicine. For example, such predictions could help determine which cancer patients would actually benefit from chemotherapy, avoiding unnecessary toxicity.