Background: Cell‑Dissociation Structural Loss Noise and Bottlenecks in CNS Drug R&D Evolutionary Data

Conventional, linear and static genomic analysis guidelines suffer from two critical blind spots: (1) noise introduced by inevitable loss of cellular structural integrity during tissue dissociation, and (2) loss of spatial information from the multidimensional brain microenvironment. Mapping protein‑sequence homology across heterogeneous models (human and rodent) and developmental‑stage‑specific feedback flux changes have long constituted an unresolved data bottleneck in central‑nervous‑system (CNS) drug discovery. Existing analytical frameworks collapse the emergence mechanisms of heterogeneous neuronal subtypes into a simple homology baseline, leading to repeated failures in target‑validation stages for predicting viable engraftment rates and effective concentrations. Static profiling that cannot integrate the evolutionary trajectories of ohnologue genes derived from whole‑genome duplication (WGD) distorts the tempo of neuronal diversification, creating a formidable barrier to CNS therapeutic development.

Discovery: Activation of Evolutionary‑Trajectory Tracking Algorithm and Empirical Synchronization of Single‑Cell‑Scale Ohnologue Differentiation Tensor

We synchronized developmental‑stage brain single‑cell transcriptomic omics matrices from five vertebrate species and deployed an evolutionary‑trajectory algorithm that computationally tracks differentiation patterns of ohnologue generations originating from WGD. To correct inter‑species single‑cell heterogeneity, we pre‑computed differential‑equation‑based velocity constants in silico and computationally eliminated batch effects at the source. This analysis revealed that changes in binding free energy of duplicated (ohnologue) gene clusters stimulate downstream transcriptional networks in the cerebral‑cortex microenvironment, driving neuronal diversity. The findings surpass conventional single‑species mouse screening models, delineate topological fluctuations of downstream transcriptomic networks, and provide precise validation of molecular‑evolutionary integrity. Notably, we mathematically modeled the functional‑gain mechanisms of ohnologue pairs, confirming the fidelity of cell‑differentiation processes.

Ohnologue Differentiation Pathway Tuning and Reversible Neuro‑Homeostasis Precision Stratification Model

These mechanistic insights enabled the construction of a patient‑cohort precision‑stratification model centered on the modulation of specific ohnologue differentiation pathways and reversible control of neuro‑homeostasis. Leveraging single‑cell‑level omics matrices, we mapped individual molecular phenotypes and familial neuro‑genetic variants to achieve ultra‑high‑resolution precision stratification of patient groups via a computational architecture. Furthermore, we designed an algorithm to up‑ or down‑clamp synaptic signaling velocity constants, establishing a backbone that reversibly maintains homeostasis even within genetically stressed microenvironments. By reversibly regulating calcium‑channel and receptor‑protein activity downstream of duplicated‑gene differentiation trajectories, the system offers a computational systems‑biology solution to restore developmental defects in a patient‑specific manner during neuronal therapeutic interventions.

Outlook: Establishing Programmable Computational‑Systems‑Biology Standards and Enabling Next‑Generation IND Digital Governance

The platform resets CNS disease R&D governance—from reliance on post‑symptomatic therapies to an AI‑driven, multidimensional‑tensor programmable infrastructure. When integrated into global pharmaceutical and biotech pipelines, the ohnologue‑mapping data serve as genetic‑gradient correction coefficients during high‑throughput screening, eliminating batch‑to‑batch production variance and providing an exclusive computational moat. The cell‑type standardization technology satisfies companion‑diagnostic (CDx) specifications, accelerating FDA and other international regulatory reviews of IND dossiers and cGMP commercial‑manufacturing approvals. Consequently, the platform constitutes a core digital‑governance asset that maximizes the probability of obtaining regulatory clearance for next‑generation digital health and gene‑therapy products.