Cognitive materials engineering represents an emerging paradigm in which artificial intelligence (AI) is incorporated directly into the design, selection, and operation of bioelectrical and bioelectronic materials. Rather than relying on static material properties or fixed stimulation protocols, AI-enabled material systems can adapt their electrical, mechanical, and chemical characteristics in response to dynamic biological states. This perspective examines how machine learning, deep learning, reinforcement learning, and neuromorphic approaches can guide material chemistry, scaffold assembly, and real-time functional tuning in bioelectronic interfaces. Focusing on neural interfaces, cancer bioelectricity, wearable biosensors, and tissue engineering, we outline how adaptive materials and predictive models may enable next-generation bioelectronic platforms that learn from biological feedback and coevolve with living tissues.
Endogenous direct-current (DC) electric fields are among the earliest signals generated after tissue injury and act as a directional cue for repair. Fields of physiological magnitude (∼40–200 mV/mm) guide cell migration (galvanotaxis), and disrupting them impairs healing while restoring them can accelerate it. Translation to tendon has lagged behind skin and bone. A conceptual fault line divides the clinical field: needle-based electrolysis therapies act by high-intensity electrolytic ablation that provokes a controlled inflammatory repair, whereas percutaneous bioelectric current stimulation (PBCS) delivers sub-milliampere DC (<500 µA) intended to act at the signaling, not the tissue-destruction, level. We review the biophysics of injury currents, the galvanotaxis of fibroblasts and tenocytes, preclinical DC effects on tendon healing, and the neurovascular pathobiology of tendinopathy, and argue that PBCS protocols should be tailored to the neurovascular ingrowth and disorganized collagen that characterize tendinopathy, instead of copying protocols developed for tissue-destroying (ablative) therapies.
This review explores the emerging integration of natural agents with electroporation (EP) techniques in oncology. Natural compounds derived from plants, microbes, and marine organisms hold promise for cancer treatment due to their multifaceted mechanisms of action, including apoptosis induction and inhibition of tumor progression. EP, a method that enhances cell membrane permeability, facilitates the efficient intracellular delivery of these bioactive compounds. This synergistic approach has demonstrated significant efficacy in preclinical studies and is progressing toward clinical application, particularly for cancers resistant to conventional therapies. We provide an in-depth analysis of this innovative therapeutic strategy’s mechanisms, preclinical evidence, and clinical progress, highlighting its potential to transform cancer treatment paradigms. Future research directions are proposed to optimize and personalize these integrative therapies.
Bioelectric signaling regulates development, regeneration, and cancer suppression. This computational study introduces a minimal control-logic framework bridging ion channel pharmacology to multicell bioelectric patterning in regeneration (e.g., Xenopus limbs, planarian polarity). Using dimensionless ordinary differential equations anchored to pharmacological timescales (e.g., propranolol PK, hours), we modeled three ion channel drugs (amiloride, propranolol, ivermectin) in single cells, embedded them in gap-junction-coupled 10 × 10 tissue lattices, and applied DEAP evolutionary optimization to recover target Vmem patterns. Multiple (k 1 , k 2 , k 3 ) parameter sets yielded identical tissue-scale Vmem patterns, despite perturbations such as ivermectin-induced 2 × k 3 Cl⁻ scaling. This parameter robustness positions tissues as active controllers maintaining patterning goals amid variability. Simulated legacy drugs controlled tissue-level bioelectric/voltage goal states, and different microscopic configurations were able to reach a target voltage state following in silico evolutionary optimization of a few parameters. New predictions include depolarizing Na + blockade inducing ectopic posteriorization and hyperpolarizing Cl⁻ activation restoring anterior–posterior Vmem gradients in planarian regeneration. This is falsifiable with CC2-DMPE dyes. These simulations could reframe legacy ion-channel drugs as bioelectric goal-state controllers beyond single-target inhibition, providing an extensible quantitative systems pharmacology pipeline (GitHub: Cameron-99/bioelectric-qsp-model) for multi-physics models and bioelectricity research.
