Predicting What Works: Using Modeling in Drug Discovery
Author: Sarah Yunes
Editors: Jae Yang and Rajiv P. Shrestha
The process of drug discovery and development is long and costly. The lead optimization stage is a process where compounds are modified to improve specificity, potency, and pharmacokinetic properties using quantitative pharmacology analyses. This is where the candidate molecule is finalized for clinical development and where the intrinsic medicinal potential is sealed. About 25% of the costs of drug development generally go to this stage, due to only one in twelve compounds making it through. Improving the success rate while limiting the development cost is an elusive but high priority goal. Quantitaitve systems pharmacology (QSP) modeling has been a highly useful means to achieve this goal. With this in the mind, Boston QSP featured a talk titled "Putting the Drug in Drug Development with Quantitative Translational Pharmacology" by the guest speaker Dr. Tristan Maurer, Senior Director of Translational Modeling and Simulation (M&S) at Pfizer. The event was chaired by Dr. Gianluca Nucci from Pfizer, Vice President of Early Clinical Development and Head of Clinical Pharmacology at Pfizer. Dr. Nucci currently serves on the Boston QSP’s Scientific Advisor Board.
In the talk, the development of a sodium citrate transporter inhibitor exemplifies how QSP modeling was used to improve the development success rate prior to lead optimization. Citrate is a product of the tricarboxylic acid (TCA) cycle and plays a role in regulating glucose metabolism. Reducing the citrate level could be used as a therapeutic mechanism for metabolic diseases, e.g. diabetes. Therefore, a sodium citrate transporter that brings more sodium citrate into the liver could be a desired drug target. However, in this case, the quantitative pharmacology modeling showed that the identified sodium citrate transporter was responsible for only a relatively minor fraction of intracellular citrate levels in the liver. This indicated that the amount of citrate reduction needed for therapeutic purposes was not possible by targeting this transporter. The project was terminated early in lead optimization, saving vital resources for therapeutic targets with a better chance of providing effective treatment options.
Even better, modeling can expedite the drug design process, advancing a drug candidate to clinical trials. Steglatro™ is a recent FDA-approved drug to treat type 2 diabetes. Steglatro™ inhibits a sodium-glucose transport protein, preventing glucose reabsorption by the kidney and allowing it to be excreted through urine. With the use of quantitative pharmacology modeling, an optimized clinical candidate drug was designed in half the usual time, with reduced overall cost and time commitment in the drug development.
This type of modeling is also being used to help navigate common issues in drug design that transcends any given therapeutic target. One such issue relates to the ability of drugs to distributed to sites of action within the brain. The blood-brain barrier (BBB) prevents the entry of many compounds into the brain via transporters that actively pump them back into the blood. This provides an extra challenge in drug discovery for central nervous system disease targets.To address this issue, the Translational M&S group at Pfizer developed a hybrid PBPK and machine learning approach that allows prediction of the degree of brain penetration from structure alone. This has also been combined with chemiformatics approaches (e.g. matched molecular pairs) to proactively provide insights into structural modifications that can improve brain penetration. All of this has been integrated into user-friendly web applications that provide easy access to users across the company. The eventual goal is to develop programs that incorporate all known data to assist in drug design and development, reducing the time and cost of getting drugs through lead optimization and into clinical trials.
QSP has been a buzzword in and seemingly a ticket into the pharmaceutical industry. Dr. Maurer advised that it was “counterproductive to try too hard to align themselves with buzzwords” like machine learning or systems pharmacology. He continued by saying that drug discovery is an inherently multidisciplinary endeavor and that there is no silver bullet to the challenges that we face. As such, in order to be successful, we need people that put “problem solving first” and “are able to pull from the breadth of available quantitative pharmacology approaches”. That’s one reason Dr. Maurer appreciates the Boston QSP events, saying that it is a great opportunity to meet people from diverse backgrounds. Outside of his work for Pfizer, Dr. Maurer enjoys backpacking, kayaking, fishing, and collecting old comic books with his four kids.
Dr. Maurer’s talk was followed by a mixer event where community members discussed and socialized over food and drinks.
We would like to thank Pfizer for sponsoring the event and CIC for sponsoring the venue.
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Boston QSP is a 501(c)(3) non-profit organization whose mission is to foster the sharing of QSP knowledge, challenges, solutions, and opportunities to advance the field as an interdisciplinary community in Boston.