Molecular Dynamics and Quantum Calculations: AAPS Thermo Fisher Interview, Part Two

As part of its coverage of AAPS PharmSci 360 2025, held from Nov. 9–12 in San Antonio, Texas, Pharmaceutical Technology® Group spoke with Sanjay Konagurthu, PhD, senior director,Science and Innovation, Thermo Fisher Scientific, about the presentation “AI/ML Model Informed Early-Stage Drug Development.”

In this conclusion of a two-part interview, Dr. Konagurthu talks about integrating a systematic in-silico framework to accelerate decision making regarding formulation feasibility and drug loading.

He says this framework provides insights into a final dosage form, as drug loading can impact both physical and chemical stability—and how much drug substance can be contained in a tablet or capsule.

“Drug loading is an important aspect when it comes to physical and chemical stability,” Dr. Konagurthu says. “With some of these modeling tools we have deployed and implemented, we can predict not only the favorable excipients and polymers to screen prior to any experimentation, we can also predict how much drug loading one can achieve.”

Too tight, too loose, or just right?

Dr. Konagurthu also addresses how molecular dynamics simulations and quantum mechanical calculations inform complex analysis of drug–excipient interactions.

“If the drug is bound too tight, it won't release,” he says. “If it's too loose, it doesn't really stabilize in the amorphous form. So, these are the series of calculations and various descriptors we assign in addition to the physical chemical properties ... to be able to do these calculations and narrow down the formulation design space. And all of this is done in silico.”

Click here for Part One of Dr. Konagurthu’s interview with PharmTech Group, in which he discusses how artificial intelligence and machine learning can help solve the dilemma of poor solubility.

Click here for all of our AAPS PharmSci 360 coverage.

Transcript

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

Hello, I'm Sanjay Konagurthu. I'm Senior Director of Science and Innovation at Thermo Fisher.

When we do things like quantum calculations, we look at the confirmer space. So what that does is give us insight into how a molecule folds upon itself. That gives us an idea about the accessible surface area of the molecule and the excipient polymers to interact. So that's one piece of it.

And we look at the various descriptors, like the classical hydrogen bond donors, acceptors, aromatic, hydrophobic. We look at potential energy surface charge models. These are like the sticky points of a molecule that essentially give us insight into how a molecule and an excipient or a polymer are interacting at a molecular level.

And then we look at molecular dynamic. Simulations, looking at interactions between the drug and the polymer. If the drug is bound too tight, it won't release. If it's too loose, it doesn't really stabilize in the amorphous form. So these are the series of calculations and various descriptors we assign in addition to the physical chemical properties, like I mentioned, to be able to do these calculations and narrow down the formulation design space.

And all of this is done in silico. And so we are saving an incredible amount of trial-and-error and unnecessary experimentation and getting to the goal quicker.

Drug loading is an important aspect when it comes to physical and chemical stability. With some of these modeling tools we have deployed and implemented, we can predict not only the favorable excipients and polymers to screen prior to any experimentation, we can also predict how much drug loading one can achieve.

So what that does is, our priority is, you get an insight into what your final dosage form is going to look like because the drug loading can impact both physical and chemical stability, and how much drug loading you have can impact how much drug you can get in a tablet or a capsule. Those are the interplays that we can get insight into based on these AI/ML modeling tools that we have implemented at Thermo Fisher.