How can you reduce the risk of your clinical programs with an “open house” for your drug product?

Problem statement

If I say, “Do not go to a clinical trial without knowing the microstructure of your product”, it may not be obvious to everyone. Let me visualize the decision process with an analogy between clinically trialing a tablet drug product and buying a house, neither are trivial, Figure 1.

In both cases, there is an important goal to accomplish – buying a house or pushing a tablet product through clinical trial (row A). Some mandatory requirements have to be fulfilled (row B).

When the mandatory requirements are fulfilled, no one will go ahead and buy the house yet (row C). Some structural requirements have to be met (row D). Such requirements can be essential (e.g., accessing each room easily) or quality of life dictated (bathroom dining room layout).

For a clinical trial, where a lot more is at stake, however, pharmaceutical professionals are often constrained with limited knowledge about the microstructure of the product going into the trial. Wouldn’t it be nice to “walk through” the drug product, getting some visuals and CQA quantifiers (e.g., questions in D2), before putting the tablet into the body of a patient?

Who cares?

Well, the patient, to begin with. Wouldn’t it be nice to give the patient a “walk through” on the drug product that s/he is going to “house”? Let’s say most of the patients trust us enough without the walk through. The significance of microstructure “open house” is actually beyond public PR. It can be essential to the support, success, and sustainability of the clinical trial.

The microstructures of the drug can impact performance

It can be as simple as fractures and micro-fractures on immediate release tablets[1], or as complex as amorphous dispersion of poorly soluble compounds in polymer[2],?microporous coating of controlled release drug[3], drug encapsulated long acting injectables[4], or biodegradable polymeric implants[5]. In our house analogy, the house better have good insulation if you lived through the February 2023 “The day after tomorrow” in Boston like me and Pawla (Figure 2). The drug product, in comparison, better to have uniform and defect free enteric coating if it is intended to release only through the intestine GI track.

A microstructure “insurance”

“We know our product really well.” You may argue. That’s well respected – a great deal of science and engineering effort goes into the development of a product to the stage even can be considered for a clinical trial. Let’s assume the product is made perfectly according to spec. Wouldn’t it be nice to do one more “open house” and allow some critical review from the entire team and from the regulatory perspective? Also keep in mind that a product’s life cycle is more than just a clinical trial. What if phase III of the trial has a manufacturer site change from phase II? Wouldn’t it be nice to have a digital print of the previous product to compare?

And if indeed something went wrong with the tablet, it is better to catch via some imaging before clinical trial.

Microstructure for 3Rs

If the product microstructure is well understood, in vitro testing, in vivo testing, and clinical trials may be Re-designed, Re-used, or Reduced. Don’t just take my word for it. Check out the DigiM patent[6] and FDA chief scientific office report[7].

How to open the black box?

Like walking through a house needs visual inspection, product “open house” starts with imaging. Due to complex, microscale nature of the drug product, correlative imaging often needs to be conducted, i.e., correlation of multiple scales (e.g., whole tablet at 10um resolution, while its coating at 10nm resolution); correlation of physical structures (where the pores are) with chemical species (where the drug are), and correlation of different modality (e.g., x-ray for the interior and white light photo for the exterior).

After imaging completed the digital transformation, a suite of AI analytics tools can transform the imaging data into critical quality attributes (CQAs), such as the particle size of the active ingredients, abundancy of porosity and fractures, and uniformity of coating thickness, greatly informing clinical trial decisions.

Image-based in silico modeling can then simulate drug release in various release environments, further enhancing the success rate of the clinical trial.

Closing remarks

Don’t go to a clinical trial without knowing the microstructures of your drug product. It is scientifically unsatisfying. And it can be costly.

Contact one of DigiM scientists before you start the clinical trial.

References (full list)

[1] E. Yost, P. Chalus, S. Zhang, S. Peter, A.S. Narang. Quantitative X-Ray Microcomputed Tomography Assessment of Internal Tablet Defects, Journal of Pharmaceutical Sciences 108 (5) (2019) 1818-1830.?(With Genentech)

[2] J. Gamble, M. Tobyn, S. Zhang, A. Zhu, J. Salplachta, J. Matula, T. Zikmund, J. Kaiser, P. Oberta. Characterization of the Morphological Nature of the Hollow Spray Dried Dispersion Particles Using X-ray Submicron-Computed Tomography, AAPS PharmSciTech (2021) 23-40. (With BMS)

[3] S. Zhang, G. Byrne. Characterization of transport mechanisms for controlled release polymer membranes using focused ion beam scanning electron microscopy image-based modelling, Journal of Drug Delivery Science and Technology. 60 (2021). (With Merck)

[4] A. Clark, R. Wang, Y. Qin, Y. Wang, A. Zhu, J. Lomeo, Q. Bao, D. Burgess, J. Chen, B.?Qin, Y. Zou, S. Zhang. Assessing microstructural critical quality attributes in PLGA microspheres by FIB-SEM analytics. Journal of Controlled Release (349: 7) (2022). (With the FDA and UCONN)

[5] Y. Chen, D. Moseon, C. Richard, M. Swinney, S. Horava, K. Oucherif, A. Cox, E. Hawkins, Y. Li, D. DeNeve, J. Lomeo, A. Zhu, L. Lyle, E. Munson, L. Taylor, K. Park, Y. Yeo. Development of hot-melt extruded drug/polymer matrices for sustained delivery of meloxicam, Journal of Controlled Release (342) (2022) p. 189-200. (With Eli Lilly)

[6] S. Zhang. System and method for computing drug-controlled release performance using images. US Patent No. US 11,081,212 B2. Date of Patent: Aug. 3, 2021

[7] Advancing regulatory science at FDA: Focus areas of regulatory science. https://www.fda.gov/media/161381/download