Development of Co-Amorphous Systems for Inhalation Therapy-Part 2: In Silico Guided Co-Amorphous...

Area of Science:

• Pharmaceutical Technology
• Drug Delivery Systems
• Computational Modeling

Background:

• Tuberculosis (TB) poses a global health challenge due to treatment complexities and drug resistance.
• Inhalable co-amorphous systems (COAMS) offer targeted pulmonary delivery for fixed-dose combinations, enhancing efficacy and reducing side effects.

Purpose of the Study:

• To develop and characterize inhalable co-amorphous systems (COAMS) for fixed-dose combinations of anti-tuberculosis drugs.
• To evaluate the utility of machine learning (ML) and physiologically based pharmacokinetic (PBPK) modeling in guiding COAMS formulation.

Main Methods:

• Machine learning (ML) identified rifampicin (RIF)-moxifloxacin (MOX) and RIF-ethambutol (ETH) combinations.
• Physiologically based pharmacokinetic (PBPK) modeling estimated therapeutic lung doses and molar ratios.
• COAMS were prepared via spray drying and co-milling, followed by physicochemical and aerodynamic characterization.

Main Results:

• Rifampicin-moxifloxacin (RIF-MOX) COAMS were successfully prepared in various ratios; RIF-ETH COAMS were not feasible for therapeutically relevant ratios.
• Spray drying yielded RIF-MOX COAMS with superior morphology, aerosolization performance (FPF > 74%), and dissolution compared to co-milling.
• PBPK modeling and ML proved effective in developing COAMS for pulmonary delivery of oral APIs.

Conclusions:

• COAMS are a viable strategy for pulmonary delivery of anti-TB drugs, particularly when drug ratios align with PBPK predictions.
• The effectiveness of COAMS may be limited when therapeutic lung dose ratios deviate significantly from 1:1, necessitating alternative delivery approaches.