Researchers have developed a novel approach to improve the accuracy of near-infrared spectroscopy (NIRS or NIR) in quantifying highly porous, patient-specific drug formulations. By combining machine learning with advanced Raman imaging, the study enhances the precision of non-destructive pharmaceutical analysis, paving the way for better personalized medicine.
Researchers applied machine learning (ML), specifically support vector regression (SVR), to improve predictive accuracy. Their results demonstrated that SVR outperformed traditional linear partial least squares (PLS) regression by reducing prediction errors by 19%. The combination of NIR and SRS microscopy provides a comprehensive understanding of porous drug matrices, enabling more accurate and reproducible dose quantification.
The findings have significant implications for patient-specific drug manufacturing. The tunable modular design (TMD) approach, previously proposed by the researchers, integrates freeze-dried polymeric modules with inkjet printing to create customized antidepressant doses. Given that antidepressant tapering requires precise, often sub-milligram dosage adjustments, robust and reliable quantification is essential.