(M1230-08-54) Optimizing Medication Regimens with Robo-Pharmacist: A Cost-Effective Approach to Reduce Polypharmacy and Improve Adherence

Purpose: Polypharmacy is a prevalent condition, affecting 10% of all patients and 30% of elderly patients in the United States. Patient non-adherence to prescribed medication regimens is a significant concern, with estimates suggesting that the cost of non-adherence is around $200 billion per year in the US and €125 billion per year in the EU. The development of innovative technologies to improve medication adherence and simplify medication regimens is therefore crucial. In this context, the purpose of this work is to introduce and explain the new technology known as "Robo-Pharmacist". This technology is designed to automatically manufacture personalized medicines consisting of multi-component mixtures of several Active Pharmaceutical Ingredients (APIs), with the aim of reducing the number of dosage forms for the patient and adjusting the doses of individual APIs according to the patient's needs. Our research demonstrates the potential of Robo-Pharmacist technology to improve patient outcomes and quality of life while reducing the cost of medication non-adherence.

Methods: To achieve the goal of improving medication adherence and simplifying medication regimens, we developed and tested a new technology called "Robo-Pharmacist. This technology uses precise automatic compounding of sub-units (solid dosage forms from 0.1 to 3 mm in diameter) into hard gelatin capsule to create personalized medicines consisting of multi-API mixtures. Each sub-unit contains one type of API and is formulated to meet the conditions of proportionality and bioequivalence with the reference listed single-drug dosage form (RLDs). We conducted a series of experiments to evaluate the accuracy and reproducibility of the automatic compounding process, as well as the equivalence with RLDs and stability of the resulting capsules.

Results: Our results indicate that the RoboPharmacist technology has significant potential for improving medication adherence and simplifying medication regimens for patients with polypharmacy. Our findings show that the compounding technology is highly accurate and reproducible. We also found that the capsules produced by the technology are equivalent in in-vitro release properties with RLDs, as measured by pharmacokinetic parameters. Furthermore, stability testing showed that the capsules exhibit better stability results than RLDs. These findings suggest that RoboPharmacist technology has significant potential for improving medication adherence and reducing the number of dosage forms that patients must take, thereby improving patient outcomes and reducing healthcare costs.

Conclusion: In conclusion, the RoboPharmacist technology has the potential to improve medication adherence and simplify medication regimens for patients with polypharmacy. By using precise automatic compounding of sub-units into hard capsules, the technology can produce personalized medicines consisting of multi-API mixtures on an industrial scale. The technology has significant potential for reducing the number of dosage forms that patients must take and adjusting the doses of individual APIs according to the patient's needs. By improving medication adherence, the Robo-Pharmacist technology could reduce the cost of non-adherence for healthcare systems, potentially saving billions of dollars per year. The development of the RoboPharmacist technology represents a significant step forward in the field of personalized medicine.

Acknowledgements: We would like to express our sincere gratitude to the partner companies who provided invaluable support throughout this project. Their contributions and expertise were instrumental in the successful completion of this research. We would like to acknowledge Zentiva k.s., Safetec s.r.o., Elmor Ltd., and Festo s.r.o for their collaborative efforts and for sharing their resources and knowledge with us.

Funding: This research was supported by the National Institutes of Health under grant number GX19-26127X. Conflict of Interest: The authors declare no conflict of interest in relation to this research.

Robo-Pharmacist Belt Assembly

Robo-Pharmacist Nest Assembly

Accuracy and Reproducibility - Comparison of counting and QA mechanism results