Soft tissues mechanical characterization is a key process in a variety of wide ranging biomedical and bioinspired technological applications. With this respect, different schools have gained popularity and they are distinguished by their take on soft materials modelling. Chiefly, the phenomenological approach tackles the issue of reproducing the macroscopic mechanical behaviour by developing suitable mathematical forms which allow for covering as general as possible stress-strain curves. On the other hand, a considerable endeavour has been put in understanding the physics underlying macroscopic properties. Such efforts address the material scientist in reducing and interpreting calibration material parameters and ensuring fundamental thermodynamic correspondence between the involved scales. Within this contribution, we adopt and enrich a previously presented microstructurally motivated framework [1, 2]. In particular, here we develop large deformation constitutive models by making assumptions on folding/unfolding processes of macromolecular networks for arriving at predicting macroscale behavior of biodegradable materials for biomedical application, such as suture threads and hydrogels. Experimental tests from our group and from literature are used as benchmarks and validation. Our theoretical statements are leveraged for reducing the number of model parameters, interpreting their role and bounds and predicting the mechanical response to different deformation classes. Mullins effect and induced anisotropy are among the most prominent phenomena which arise from our models. REFERENCES [1] F. Trentadue, D. De Tommasi and G. Puglisi. A predictive micromechanically-based model for damage and permanent deformations in copolymer sutures. Journal of the Mechanical Behavior of Biomedical Materials (2021). [2] G. Vitucci, D. De Tommasi, G. Puglisi and F. Trentadue. A predictive microstructure inspired approach for anisotropic damage, residual stretches and hysteresis in biodegradable sutures. International Journal of Solids and Structures (2023).