Nanomedicines for controlled drug release provide temporal and spatial regulation of drug bioavailability in the body. The timing of drug release is usually engineered either for slow gradual release over an extended period of time or for rapid release triggered by a specific change in its physicochemical environment. However, between these two extremes, there is the desirable possibility of adaptive nanomedicines that dynamically modulate drug release in tune with its changing environment. Adaptation and response through communication with its environment is a fundamental trait of living systems; therefore, the design of biomimetic nanomedicines through the approaches of bottom-up synthetic biology provides a viable route to this goal. This could enable drug delivery systems to optimize release in synchronicity with the body's natural biological rhythms and the personalized physiological characteristics of the patient, e.g. their metabolic rate. Living systems achieve this responsiveness through feedback-controlled biochemical processes that regulate their functional outputs. Towards this goal of adaptive drug delivery systems, we review the general benefits of nanomedicine formulations, provide existing examples of experimental nanomedicines that encapsulate the metabolic function of enzymes, and give relevant examples of feedback-controlled chemical systems. These are the underpinning concepts that hold promise to be combined to form novel adaptive release systems. Furthermore, we motivate the advantages of adaptive release through chronobiological examples. By providing a brief review of these topics and an assessment of the state of the art, we aim to provide a useful resource to accelerate developments in this field. Impact statement The timing and rate of release of pharmaceuticals from advanced drug delivery systems is an important property that has received considerable attention in the scientific literature. Broadly, these mostly fall into two classes: controlled release with a prolonged release rate or triggered release where the drug is rapidly released in response to an environmental stimulus. This review aims to highlight the potential for developing adaptive release systems that more subtlety modulate the drug release profile through continuous communication with its environment facilitated through feedback control. By reviewing the key elements of this approach in one place (fundamental principles of nanomedicine, enzymatic nanoreactors for medical therapies and feedback-controlled chemical systems) and providing additional motivating case studies in the context of chronobiology, we hope to inspire innovative development of novel "chrononanomedicines."

The transmission of chemical signals via an extracellular solution plays a vital role in collective behavior in cellular biological systems and may be exploited in applications of lipid vesicles such as drug delivery. Here, we investigated chemical communication in synthetic micro- and nanovesicles containing urease in a solution of urea and acid. We combined experiments with simulations to demonstrate that the fast transport of ammonia to the external solution governs the pH–time profile and synchronizes the timing of the pH clock reaction in a heterogeneous population of vesicles. This study shows how the rate of production and emission of a small basic product controls pH changes in active vesicles with a distribution of sizes and enzyme amounts, which may be useful in bioreactor or healthcare applications.

Peptide epitopes mediate as many as 40% of protein− protein interactions and fulfill signaling, inhibition, and activation roles within the cell. Beyond protein recognition, some peptides can self-or coassemble into stable hydrogels, making them a readily available source of biomaterials. While these 3D assemblies are routinely characterized at the fiber level, there are missing atomistic details about the assembly scaffold. Such atomistic detail can be useful in the rational design of more stable scaffold structures and with improved accessibility to functional motifs. Computational approaches can in principle reduce the experimental cost of such an endeavor by predicting the assembly scaffold and identifying novel sequences that adopt said structure. Yet, inaccuracies in physical models and inefficient sampling have limited atomistic studies to short (two or three amino acid) peptides. Given recent developments in machine learning and advances in sampling strategies, we revisit the suitability of physical models for this task. We use the MELD (Modeling Employing Limited Data) approach to drive self-assembly in combination with generic data in cases where conventional MD is unsuccessful. Finally, despite recent developments in machine learning algorithms for protein structure and sequence predictions, we find the algorithms are not yet suited for studying the assembly of short peptides.