摘要
In an effort to promote the availability of safe and effective drugs, the US Food and Drug Administration is developing spectroscopic methods to assess the quality of drugs in the field. Here we report a rapid screening classification method for Tamiflu (oseltamivir phosphate) capsules using a portable Raman spectrometer to perform screening on three solid oral dosage strengths of Tamiflu, 30 mg, 45 mg and 75 mg. Tamiflu is an antiviral drug that is stockpiled for use in the event of pandemic influenza outbreak. The qualitative classification methods reported were developed using the Raman spectra of intact capsules. The classification algorithms used were able to reliably distinguish the three dosage strengths of Tamiflu. These qualitative models are validated with additional Tamiflu samples from different batches and simulated counterfeits of Tamiflu. The probability that a test sample belongs to each dosage strength class is calculated, and strict class predictions are used to assign each sample to a particular class. The classification methods reported here enable development of user-independent, field-deployable methods for finished drug products and are able to correctly assign 92% of the validation samples using authentic Tamiflu and 100% of the simulated counterfeits.
In an effort to promote the availability of safe and effective drugs, the US Food and Drug Administration is developing spectroscopic methods to assess the quality of drugs in the field. Here we report a rapid screening classification method for Tamiflu (oseltamivir phosphate) capsules using a portable Raman spectrometer to perform screening on three solid oral dosage strengths of Tamiflu, 30 mg, 45 mg and 75 mg. Tamiflu is an antiviral drug that is stockpiled for use in the event of pandemic influenza outbreak. The qualitative classification methods reported were developed using the Raman spectra of intact capsules. The classification algorithms used were able to reliably distinguish the three dosage strengths of Tamiflu. These qualitative models are validated with additional Tamiflu samples from different batches and simulated counterfeits of Tamiflu. The probability that a test sample belongs to each dosage strength class is calculated, and strict class predictions are used to assign each sample to a particular class. The classification methods reported here enable development of user-independent, field-deployable methods for finished drug products and are able to correctly assign 92% of the validation samples using authentic Tamiflu and 100% of the simulated counterfeits.
