Issue 2 Volume 15 (2026)

Autonomous experimental systems are progressing from isolated automation toward integrated platforms capable of proposing hypotheses, selecting tools, executing computational or physical procedures, interpreting observations, and modifying subsequent actions. In translational pharmacology, these capabilities create governance problems that cannot be resolved by model accuracy, conventional laboratory safety controls, general human oversight, or reporting requirements alone. This Original Autonom

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Pharmaceutical translation is commonly described through development stages, publications, patents, candidate progression, or clinical milestones, yet these markers do not establish whether an originating laboratory claim has become reproducible, biologically relevant, manufacturable, pharmacologically credible, or meaningful for patients. This article develops an original Pharmaceutical Translation Observatory as a non-empirical evidence architecture for tracking these distinct transitions with

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Next-generation pharmaceutical platforms can generate compounds, models, assays, manufacturing processes, and human-relevant experimental evidence with increasing speed and technical sophistication. Yet platform performance is frequently interpreted as evidence of translational maturity before product-specific quality, biological consequence, pharmacological behaviour, clinical utility, or implementation feasibility has been established. This article develops an original Translational Decision A

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Biopharmaceutical manufacturing increasingly uses mechanistic models, statistical predictors, process analytical technologies, metabolic reconstructions, and digital-twin concepts to anticipate culture behavior and product quality. However, an accurate predictor of a selected process variable is not necessarily a model of the evolving manufacturing system. It may lack a persistent representation of cell state, explicit intervention semantics, multiscale temporal dynamics, calibrated uncertainty,

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Pharmacovigilance systems operate within changing medicinal-product, population, data, analytical, and organizational environments. Static surveillance configurations may consequently become misaligned with the products being monitored, the populations exposed, the information captured, or the decisions supported. This article develops an original adaptive-governance framework for pharmacovigilance under model drift, product evolution, data-source change, and emerging patterns of medicine use. T

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Precision pharmacotherapy requires decisions about what dose should be administered next, not merely forecasts of what is likely to occur under previously observed treatment patterns. Current patient models can integrate pharmacokinetic, pharmacodynamic, laboratory, physiological, and treatment data, yet their predictions may remain associational when treatment is repeatedly adapted in response to evolving patient states. Delayed efficacy and toxicity, incomplete clinical observation, treatment-

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Artificial intelligence is increasingly used to predict pharmacokinetic parameters, characterize exposure–response relationships, select population models, interpret therapeutic drug-monitoring data, and support individualized dosing. However, analytical performance alone does not explain why apparently similar systems succeed in one setting, fail in another, or create unintended clinical consequences. This realist review examines how data, model, patient, organizational, and decision contexts i

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Clinical pharmacokinetic and pharmacodynamic modelling supports the interpretation of dose, exposure, biological response, and patient variability, but most models remain tied to a particular drug, population, sampling design, endpoint, or clinical task. This task-specific orientation limits reuse across evolving treatment contexts and does not inherently address informative observation processes, distribution shift, or uncertainty under transfer. This Original Foundation-Model Architecture Arti

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Biomanufacturing cells are increasingly engineered with synthetic regulatory circuits, dynamic metabolic controls, automated design workflows, and process-aware sensing. Nevertheless, individual engineering improvements do not by themselves create manufacturing systems capable of coordinating metabolic state, recombinant-protein processing, product quality, productivity, and robustness across scales. This article proposes an evidence-bounded programmable biomanufacturing-cell architecture as an

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Medicine-safety evidence is distributed across spontaneous case reports, clinical records, registries, observational studies, scientific publications, regulatory assessments, product labels, and manufacturing or product histories. These sources differ in structure, terminology, provenance, temporal coverage, jurisdiction, and evidentiary meaning, limiting the reconstruction of how an emerging concern develops into a safety signal, causal assessment, regulatory response, or product change. This o

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Cell-dependent biomanufacturing has enabled diverse therapeutic products, yet its reliance on living production hosts can constrain rapid process reconfiguration, local production, and transfer across therapeutic modalities. Cell-free pharmaceutical biotechnology removes the requirement to maintain an intact production cell, but current evidence remains fragmented across reaction engineering, template design, post-translational processing, purification, stabilization, and distributed implementat

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Human-relevant organoids, tissue models, and microphysiological systems increasingly reproduce biological features that conventional cultures or animal models represent incompletely. Yet biological resemblance is not equivalent to pharmacological qualification. A model can preserve human genetics, architecture, mechanical cues, or disease phenotypes while failing to reproduce clinically relevant exposure, target engagement, response kinetics, toxicity, interindividual variability, or interlabora

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Polypharmacology is usually represented as a relationship between one chemical structure and several targets, yet this representation suppresses a central physical complication: each target is itself an ensemble of interconverting conformations, and ligand recognition may occur through different subsets of those ensembles. Consequently, apparent multi-target activity can arise from different binding-competent states, different population shifts, and different kinetic pathways rather than from a

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Computational pharmacology has become increasingly effective at predicting bound poses, relative affinities, and equilibrium free-energy differences, yet these endpoint quantities describe only part of molecular recognition. A ligand can reach the same apparent bound state through different association routes, populate distinct encounter or metastable complexes, cross different rate-limiting barriers, and leave through dissociation pathways that are not simply the reverse of association. These f

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Molecular dynamics simulation has become capable of resolving conformational states, ligand-binding pathways, kinetic events, allosteric coupling and binding thermodynamics at resolutions that increasingly intersect with medicinal-chemistry decisions. Yet greater trajectory length, algorithmic sophistication or apparent molecular detail does not by itself establish that a simulated observation is sufficiently reliable, mechanistically interpretable or transferable to support compound selection.

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Quantitative structure–activity relationship (QSAR) models are often interpreted as if accurate prediction were evidence that a molecular feature participates mechanistically in producing activity. That inference is not generally warranted. Predictive models can exploit correlations arising from scaffold structure, assay context, sampling, representation choice, redundancy, or hidden common causes without identifying the variables through which a structural intervention changes a biological outc

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