Shifting from Trial-and-Error to Rational Design Drives Efficiency in Pharmaceutical Formulation

*Full transcript available below

In an interview with Pharmaceutical Technology® at American Association of Pharmaceutical Scientists (AAPS) PharmSci 360, Nathan Bennette, director, Scientific and Technical Advisory, Catalent Pharma Services, discusses a paradigm shift toward rational design—a method grounded in conceptual and mechanistic modeling. According to Bennette, the rational design approach brings clarity and efficiency to the development of new drug formulations.

Instead of the traditional trial-and-error method, Bennette advocates a structured approach. “The rational design concept is to use models—conceptual models and mechanistic models—to develop more focused hypotheses and then targeted experimentation to more efficiently get at the solution,” he explains. This shift begins with the creation of conceptual models to understand the problem in depth, followed by mechanistic (quantitative or mathematical) models that reveal the fundamental physical and chemical processes at play, he explains.

Semi-empirical models, often realized via computer simulations, Bennette notes, further refine hypothesis testing and solution optimization. Bennette shares a recent example in which his team faced a drug with poor oral absorption. Through in-vitro experimentation, the team isolated the rate-limiting step—diffusion across the unstirred water layer—and designed formulations specifically to enhance that process. Bennette notes that the in-vitro improvements correlated well with in-vivo tests.

How are rational design models transforming pharmaceutical problem solving?

Bennette believes this efficiency-first mindset yields tangible industry advantages. “If you can come up with these really focused hypotheses and work on those problem statements, you get there faster. You get there with less expenditure of time and energy and resource, and, ultimately, I think you come up with a better product,” he says.

Bennette adds that, as pharmaceutical companies face mounting pressure to deliver innovations with speed and efficacy, rational design frameworks promise to shape industry standards for problem solving and formulation optimization.

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About the speaker

Nathan Bennette, Director, Scientific and Technical Advisory, Catalent Pharma Services

Bennette is a subject matter expert on establishing developability and non-parenteral formulation strategies for poorly bioavailable molecules, including protein degraders and others described as “beyond rule of 5”. He has worked in drug development for over 15 years in a range of technical and leadership roles and has broad experience with formulation of small-molecule therapeutics. Bennette applies his passion for mechanistic understanding of drug delivery to design systems tailored to each molecule and target product profile, utilizing a range of oral technologies such as amorphous solid dispersions, controlled-release platforms, nanocrystalline formulations, multiparticulates, and lipid-based systems. He has experience working across the entire drug development lifecycle from preclinical and early phase development to late phase clinical studies and commercialization.

Transcript

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

Hi, Nathan Bennette, I work in our scientific advisory function at catalent pharma solutions, and my role there is to be sort of an in house consultant working with customers to define the problem statement and develop technical strategy for formulations.

So in sort of traditional problem solving and pharma development and formulation and process development in particular, a lot of times, we have historically done a lot of trial and error, where you form a hypothesis, but then you run a large number of experiments trying everything under the sun to develop a solution to the problem. The rational design concept is to use models, conceptual models and mechanistic models to develop more focused hypotheses and then targeted experimentation to more efficiently get at the solution.

The question, it's principally around efficiency. Those trial and error experimentation methods require huge amounts of effort and to arrive at a robust solution eventually. But these mechanistic, driven, hypothesis, driven experiment designs allow us to use the best practices in science to build out our understanding of the system and more efficiently come to a more optimal solution.

I think of it in sort of three layers. First, you have conceptual models, you have to form a picture in your head of what is actually happening in the system that you're studying. Once you've done that, you can start to form a real hypothesis about what might be going wrong with your with your molecule, for example, as you're trying to develop your formulation, all formulation development is a problem solving exercise, and it's important to know what problem you're trying to solve. So this conceptual model allows you to have a framework for forming a hypothesis. Then there's another level that I call mechanistic models. Some people call them quantitative models, mathematical models. These are the basic physics that underlie the physical situation of your drug molecule and the question you're trying to answer maybe it's from an absorption perspective or from a stability perspective. And so once you have your quantitative mechanistic model, you can start to form a more specific hypothesis. How fast does this process happen to? What extent can we expect this process to proceed. And then finally, we have sort of empirical or semi empirical models where that mechanism and concept have been reduced to an actual computer program, and you can run simulations. So these tools are all available for a range of different questions that are relevant to pharma development, like pharmacokinetic absorption or chemical stability or process modeling for different pharmaceutical manufacturing processes. So we use the starting from conceptual to mechanistic to semi empirical models. We use this whole framework to describe the problems that we're trying to solve and develop actual formulations to resolve that.

I have a really good one from recent work that does both. It helped us to get much more quickly to an optimized solution that actually we tested it out in vivo, and it worked really well. So we were developing this compound that had very, very poor oral absorption. And we asked the question first, why? And we started with the conceptual model that every molecule has to get into the GI tract. It has to dissolve it then has to diffuse across the unstirred water layer, up against the epithelium and then partition across the epithelium to get into systemic absorption. So that's our conceptual model. And we asked, Well, why is this molecule having a hard time doing that? What? What is the fundamental rate limiting step? And we learned through a series of in vitro experiments that the rate limiting step was diffusion across that unstirred water layer, the mucus layer. And so with that in mind, we didn't, we didn't go out and try every formulation concept under the sun that would be a trial and error approach. Let's try this. Let's try this. Let's try this. We said, Okay, we know the key problem is diffusion rate. So what formulations can we develop that enhance the diffusion rate? Because that's the key step. So we went in the lab and we developed a few formulations that we proposed would help with that, and we tested them in vitro and demonstrated that they did, in fact, enhance the diffusion rate. And then we took the best of that set into an in vivo study. And what was really cool was that the extent to which. We enhanced diffusion with those formulations, in vitro correlated very well with the in vivo enhancement factor as well. So we saw, we figured out what the right problem was. We developed a conceptual framework for how to resolve that, and we went and tested that specific hypothesis, and much more quickly, much more efficiently, came to a workable solution.

I think I've been hitting a lot on the efficiency, the efficiency of the process of coming with a workable formulation. I think that translates to faster timelines if you're if you're spending enormous amounts of time trying everything in order to find the one thing that works. Then you spend a lot of resource. You spend a lot of time working on stuff that isn't isn't going to be the answer. But if you can come up with these sort of really focused hypotheses and work on those problem statements, you get there faster. You get there with less expenditure of time and energy and resource and ultimately, I think you come up with a better product. It's a it's a more optimized outcome.