As with any drug, approval of generic versions of macromolecular drugs requires rigorous evaluation of therapeutic equivalence to the reference drug in order to assure similar efficacy and safety. Compared with drugs that are small organic molecules, the chemical composition and characteristics of macromolecular drugstypically proteins or polysaccharidesare inherently more variable because of the way these molecules are produced and isolated. It would be advantageous to have a way to determine molecular similarity and, by implication, equivalence without using costly in vivo testing in animals and humans.
The specific aim of the proposal is to develop and test a robust data-driven statistical methodology for assessing similarity among distinct samples of therapeutic macromolecules, whether from different batches or altered processes, or even if produced by different entities entirely. The methodology is based on a genetic algorithm designed to extract relevant features from large, complex datasets. The hypothesis guiding the work is that with proper data interpretation and modeling, therapeutic equivalence can be inferred from molecular similarity determined using spectroscopic and chromatographic measurements that reveal the critical molecular attributes ultimately responsible for important properties of the generic and reference macromolecules: efficacy, side-effects, stability, and so on. Even without knowing how these attributes specify these properties, the fact that they do specify them means that this data is where equivalence can best be determined without going to in vivo testing. Data introduced into the model will be collected for multiple lots and batches of protein and polysaccharide drug substances and products. The data will come from an assortment of high-resolution mass-spectrometry methods, high-field nuclear magnetic resonance spectroscopy analyses, and several other spectroscopic and chromatographic methods used to characterize macromolecules in solution. Biological activity data will be obtained from outsource testing labs that have established assays in place. The outcome of the proposed research will be a working methodology for evaluation of similarity between generic and reference versions of any macromolecular drug. This will lead towards faster approval of generic versions of macromolecular drugs, and thereby to broader access and lower cost for these drugs which are critical to treating many serious diseases.
Before a generic version of a drug is introduced, its similarity to the original drug must be carefully evaluated to ensure safety and efficacy. Determination of similarity is challenging for macromolecular drugs, whose active ingredient is a large protein or carbohydrate molecule, and may require lengthy and costly testing in animals or humans. This project will develop a new method that could be used to evaluate similarity using only results from cell culture and laboratory instruments, thereby avoiding animal or human testing, streamlining the approval process, and bringing a cheaper drug to market that is as safe and effective as the original.