Issue 4 Volume 15 (2026)

Biosimilar switching is often discussed as a single intervention, although reference-to-biosimilar, repeated reference–biosimilar alternation, biosimilar-to-biosimilar switching, and return to a previous product represent different exposure histories. Combining these sequences can make reassuring evidence appear more transferable than the underlying studies permit. To critically synthesize recent clinical and review-level evidence on biosimilar switching and identify which conclusions are direct

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Programmable therapeutic cells challenge a central assumption of conventional pharmacology: that dose is administered as a chemically defined quantity whose subsequent exposure can be separated from the machinery that produced it. Engineered mammalian cells and microorganisms instead remain biologically active after administration. They may sense endogenous or environmental signals, change state, execute genetic logic, migrate or persist in selected compartments, and generate therapeutic molecul

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Artificial intelligence is increasingly capable of extracting process state from multivariate bioprocess data, forecasting trajectories, and proposing or executing control actions. Yet an optimization system can be technically sophisticated while pursuing the wrong objective. In biopharmaceutical manufacturing, maximizing titer, throughput, or equipment utilization does not by itself establish preservation of product quality. This Systems Perspective argues that AI-enabled control should therefo

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Digital twins are increasingly positioned as enabling technologies for model-based pharmaceutical biotechnology, yet many proposed architectures treat biological uncertainty as a nuisance to be averaged away rather than as part of the state that must be represented. This distinction matters because a bioprocess may remain numerically predictable while its underlying cell population, model structure, sensor validity, or scale-dependent behavior changes in ways that alter the meaning of that predi

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Precision pharmacotherapy is often framed as the selection of an individualized dose from pretreatment characteristics. That framing is incomplete because treatment itself generates new information. Measured concentrations, early pharmacodynamic responses, toxicity signals, evolving organ function, inflammatory state, adherence, and other time-varying features can make an initially reasonable PKPD prediction progressively less representative of the patient. This Perspective argues that precision

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Target-mediated drug disposition (TMDD) is commonly interpreted as a pharmacokinetic consequence of saturable binding between a drug and a finite-capacity pharmacological target. That formulation is indispensable but incomplete when treatment changes the biological state that determines subsequent disposition. Target abundance can vary across tissues and individuals, target turnover can create delayed recovery, pharmacological action can deplete or expand accessible target pools, and disease imp

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Model-informed drug development (MIDD) now supports decisions spanning regulatory interaction, dose selection, population extrapolation, exposure–response assessment, trial design, formulation bridging, and individualized dosing. Yet evidence is often organized by modeling technique rather than by the decision a model is intended to support. This can obscure differences in validation requirements, population transportability, measurement dependence, and acceptable inferential strength. To map co

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Artificial intelligence is increasingly positioned to accelerate pharmacovigilance by extracting adverse-event information, classifying cases, ranking candidate signals, and directing human attention. Yet these functions are often evaluated primarily through discrimination, retrieval, or benchmark accuracy, even though operational pharmacovigilance decisions depend on a different question: whether an output can be interpreted reliably in the population, data source, time period, and decision con

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Drug safety surveillance increasingly combines spontaneous reports, longitudinal healthcare data, distributed data networks, and computational methods, yet the inferential structure connecting these resources remains under-specified. A recurring problem is that treatment is not allocated independently of prognosis. Indication, disease severity, prior treatment history, channeling toward newer or alternative therapies, switching, treatment-confounder feedback, and informative discontinuation can

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Pharmacovigilance increasingly operates across data environments that differ fundamentally in how exposures, adverse events, populations, denominators, time, and clinical context are represented. Spontaneous reporting remains indispensable for early detection, but signal interpretation increasingly requires longitudinal healthcare data, registries, and distributed analytical networks. Conventional episodic reviews poorly accommodate the continual arrival of new signals, methodological changes, a

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Translational pharmaceutical research is commonly organized around experiments that are optimized locally: targets are selected for biological plausibility, models for mechanistic sophistication, doses for acceptable exposure, biomarkers for measurable change, and early trials for interpretable signals. Yet a technically strong result at one layer has limited developmental value when the inference connecting it to the next layer is weak. This Perspective argues that translational performance sho

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Early clinical development is commonly organized as an accumulation of favorable observations: adequate exposure, target engagement, pharmacodynamic activity, tolerability, and eventually evidence of clinical benefit. That sequence is operationally useful but can create an inferential problem when each positive result is treated as another layer of confirmation without specifying which mechanistic claim has actually survived a credible opportunity to fail. This Perspective proposes a different d

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