From Guesswork to Clarity: amofor now offers mechanistic predictions of ASD drug release

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Release profiles are at the heart of successful formulations. However, drug formulation scientists struggle with them. Weeks of trial-and-error experiments. Unpredictable outcomes. No mechanistic understanding.
amofor is now changing this.
Formulators can use a drug release forecasting tool that delivers unprecedented accuracy, enabling better formulations in record time and at reduced cost.

The Challenge of ASD Formulations

The pharmaceutical industry is increasingly confronted with poorly soluble drugs; the majority of the molecules in the discovery pipeline are poorly water-soluble and, thus, insufficiently bioavailable. This presents significant hurdles in formulating oral medications that can be effectively absorbed in the gastrointestinal tract. Amorphous Solid Dispersions (ASDs) have emerged as a popular solution. By molecularly dispersing a poorly soluble drug within a hydrophilic polymer matrix, ASDs enhance the drug’s solubility and bioavailability.

However, while ASDs improve solubility, they introduce new complexities in drug release. Unlike traditional crystalline formulations with predictable dissolution behaviors, ASDs have complex and variable release mechanisms (Moseson et al. 2021; Saboo et al. 2020a; Purohit et al. 2017; Saboo et al. 2020b). Variables such as drug-polymer interactions, drug load ratios, and water interactions can lead to a range of unpredictable outcomes:

  • Rapid drug release and very fast decrease in concentration
  • Very fast drug release leading to prolonged supersaturation
  • Lack of drug release at high drug loads (the “falling-off-the-cliff effect,” where drug release suddenly drops off)

Understanding these release curves beyond the classical erosion and surface release models is essential for formulators. Yet, there is no mechanistic understanding of why these release profiles vary widely. Sometimes, an ASD yields a favorable release curve; other times, it results in poor performance.

As a result, formulation scientists spend weeks and months conducting extensive solubility and dissolution testing work, trying to find the correct formulation through trial and error. This approach consumes significant financial and material resources and yields unpredictable outcomes, creating a bottleneck in the ASD development process.

Our Solution to Predict Drug Release

So, how can we address this lack of mechanistic understanding behind ASD release profiles? In 2021, we decided to tackle this question.

“Our goal was to empower formulation scientists with a digital tool to understand and predict release profiles,” says Dr. Christian Lübbert, CEO at amofor. 

We collaborated with top ASD formulation experts from the pharma industry (AbbVie) and academia (Purdue University, TU Dortmund University). Our approach was to combine PC-SAFT-based in silico modeling with cutting-edge experiments. Specifically, we have developed a physics-based forecasting model that efficiently simulates the complex phase changes within an ASD matrix, including water penetration, phase separation, and drug-polymer interactions.

The results:

  • Our calculations show excellent agreement with experimental data, validating the precision of our model.
  • The model predicts critical thresholds (level of congruency, amorphous and crystalline solubilities) and thus optimal drug/polymer ratios. For scientists, this means sustained, high-level drug exposure with minimized risk of precipitation or crystallization – transforming how ASDs are developed.
  • It brings clarity to the complex behaviors of individual polymers in ASDs.
  • The approach was among other polymers validated for PVPVA64 ASDs (Deac et al. 2024; Krummnow et al. 2022) and HPMCAS ASDs
  • It explains why certain polymers prevent precipitation and others do not (Luebbert und Stoyanov 2023)

We’ll present detailed results in two showcases demonstrating how our model accurately predicts drug release mechanisms. Stay tuned!

What’s the Impact for Drug Formulators?

amofor’s predict-first modeling approach for drug release is a game-changer for formulation scientists. By providing a mechanistic understanding of ASD release profiles, we empower formulators to:

  1. Improve Formulation Performance: Achieve optimal drug release profiles from the outset, enhancing bioavailability and therapeutic efficacy. Scientists can now instantly calculate the critical drug load point, providing immediate guidance on the optimal formulation concentration.
  2. Reduce Development Time: Move from weeks or months of experimental trials to immediate insights
  3. Cut Costs: Save financial and material resources by eliminating unnecessary experiments.

With these precise, actionable insights, we eliminate the guesswork traditionally associated with formulation development. We streamline the formulation process, allowing pharmaceutical teams to move forward confidently, knowing they have the best possible understanding of their formulations.

Predicting drug release is now part of our service portfolio.

Contact us to schedule a personalized demo and see how amofor’s predictive service can accelerate your drug formulation efforts.

References

  • Deac, Alexandru; Luebbert, Christian; Qi, QingQing; Courtney, Reagan M.; Indulkar, Anura S.; Gao, Yi et al. (2024): Dissolution Mechanisms of Amorphous Solid Dispersions: Application of Ternary Phase Diagrams To Explain Release Behavior. In: Mol. Pharm. DOI: 10.1021/acs.molpharmaceut.3c01179 .
  • Krummnow, Adrian; Danzer, Andreas; Voges, Kristin; Dohrn, Stefanie; Kyeremateng, Samuel O.; Degenhardt, Matthias; Sadowski, Gabriele (2022): Explaining the Release Mechanism of Ritonavir/PVPVA Amorphous Solid Dispersions. In: Pharmaceutics 14 (9). DOI: 10.3390/pharmaceutics14091904 .
  • Luebbert, Christian; Stoyanov, Edmont (2023): Tailored ASD destabilization – Balancing shelf life stability and dissolution performance with hydroxypropyl cellulose. In: Int. J. Pharm. X 5, S. 100187. DOI: 10.1016/j.ijpx.2023.100187 .
  • Borrmann, Dominik; Friedrich, Pascal; Smuda, Justin; Sadowski, Gabriele (2024): Counteracting the loss of release for indomethacin-copovidone ASDs. In: Journal of pharmaceutical sciences. DOI: 10.1016/j.xphs.2024.10.022 .
  • Moseson, Dana E.; Corum, Isaac D.; Lust, Andres; Altman, Kevin J.; Hiew, Tze Ning; Eren, Ayse et al. (2021): Amorphous Solid Dispersions Containing Residual Crystallinity: Competition Between Dissolution and Matrix Crystallization. In: AAPS J 23 (4), S. 69. DOI: 10.1208/s12248-021-00598-6 .
  • Purohit, Hitesh S.; Ormes, James D.; Saboo, Sugandha; Su, Yongchao; Lamm, Matthew S.; Mann, Amanda K. P.; Taylor, Lynne S. (2017): Insights into Nano- and Micron-Scale Phase Separation in Amorphous Solid Dispersions Using Fluorescence-Based Techniques in Combination with Solid State Nuclear Magnetic Resonance Spectroscopy. In: Pharm. Res. 34 (7), S. 1364–1377. DOI: 10.1007/s11095-017-2145-z .
  • Saboo, Sugandha; Kestur, Umesh S.; Flaherty, Daniel P.; Taylor, Lynne S. (2020a): Congruent Release of Drug and Polymer from Amorphous Solid Dispersions: Insights into the Role of Drug-Polymer Hydrogen Bonding, Surface Crystallization, and Glass Transition. In: Mol. Pharm. 17 (4), S. 1261–1275. DOI: 10.1021/acs.molpharmaceut.9b01272 .
  • Saboo, Sugandha; Moseson, Dana E.; Kestur, Umesh S.; Taylor, Lynne S. (2020b): Patterns of drug release as a function of drug loading from amorphous solid dispersions: A comparison of five different polymers. In:Eur. J. Pharm. Sci. 155, S. 105514. DOI: 10.1016/j.ejps.2020.105514 .