NonclinicalEdit

Nonclinical work in biomedicine and pharmaceutical development refers to activities conducted before human exposure is involved. In practice, nonclinical (often called preclinical) development comprises in vitro experiments, animal studies, and computational analyses that aim to characterize a candidate drug or medical device's pharmacology, safety, and potential risks. The nonclinical package is used to design safe starting doses for first-in-human trials, support regulatory submissions, and inform decisions about whether and how to proceed with clinical testing. In this context, nonclinical data integrate findings from pharmacology, toxicology, and safety pharmacology to form a coherent picture of how a product might behave in people. In this article, when referring to people, the terms black and white are not capitalized.

Nonclinical work sits at the boundary between discovery science and patient-facing development. It translates laboratory observations into risk assessments that guide clinical planning and investment decisions, and it is a central determinant of whether a program moves forward. The discipline blends traditional laboratory science with emerging technologies, such as computer modeling, high-throughput screening, and alternative assay platforms, all of which are designed to improve predictive accuracy while reducing time and cost.

Definitions

Nonclinical (preclinical) development: The stage of research conducted before clinical trials, including pharmacology, toxicology, pharmacokinetics, and safety assessments. See preclinical and drug development.
Pharmacology: The study of how a drug interacts with biological systems, including desired effects and potential off-target effects. See pharmacology.
Toxicology: The assessment of adverse effects and their severity, dose relationships, and target organs. See toxicology.
Safety pharmacology: Focused studies on potential effects on vital organ systems (e.g., cardiovascular, respiratory, nervous systems) to identify safety signals early. See safety pharmacology.
In vitro and in silico methods: Laboratory-based and computer-based approaches used to predict human outcomes without or with reduced use of animals. See in vitro and in silico.

Scope and components

Pharmacokinetics and ADME: Studies of absorption, distribution, metabolism, and excretion (ADME) help predict how a drug will move through the body and what exposures people might experience. See pharmacokinetics.
In vitro models: Cell-based assays, enzyme studies, and organotypic cultures that provide mechanistic insight and high-throughput screening options. See in vitro.
Animal models and translational science: Animal testing remains a core element of risk assessment, providing data on systemic exposure and potential toxicities that are not easily captured in vitro. See animal testing.
Modeling and simulation: Computational approaches, including quantitative structure–activity relationships (QSAR) and physiologically based pharmacokinetic (PBPK) modeling, help extrapolate animal data to humans and optimize study design. See in silico and PBPK.
Regulation and quality systems: Nonclinical work is governed by quality and regulatory standards, notably Good Laboratory Practice (GLP) and international guidelines. See GLP and ICH.

Regulatory framework

Regulators require a robust nonclinical package to support progression to human trials. In the United States, the Food and Drug Administration (FDA) relies on relevant ICH guidelines and adheres to GLP standards to ensure consistency and reproducibility. In the European Union, the European Medicines Agency (EMA) follows parallel standards and incorporates EU-focused risk assessments. International harmonization, led by the ICH, helps align expectations across major markets. Key guidelines include:

ICH M3(R2): Nonclinical Safety Evaluation of Biotechnological-Derived Pharmaceuticals and related products, which outlines the breadth of data needed to support clinical development. See ICH M3(R2).
ICH S6: The nonclinical safety evaluation of biotechnology-derived pharmaceuticals, emphasizing translational considerations and species selection. See ICH S6.
Good Laboratory Practice (GLP): A quality system for nonclinical studies intended to ensure data integrity and auditability. See GLP.
Regulatory submissions: The IND in the United States and the corresponding mechanisms in the EU require detailed nonclinical data to justify human testing. See FDA and EMA.

Global manufacturing and clinical development programs increasingly rely on harmonized nonclinical data to reduce duplicative testing, accelerate timelines, and improve decision-making under uncertainty. See drug development.

Models and ethics

Advances in nonclinical methods include better in vitro systems, organ-on-a-chip technologies, and computational modeling that aim to improve human relevance and potentially reduce animal use. Critics of animal testing argue that alternatives should replace animals wherever feasible, while proponents contend that certain complex systemic effects can only be observed in living organisms or require a tiered approach to risk assessment. From a policy standpoint, the pragmatic position supports a stepwise strategy: use the most informative, least burdensome methods available, while preserving the ability to detect clinically meaningful risks. This approach is intended to protect patients without stifling innovation, though disagreements persist about the pace and scope of change. Proponents emphasize that nonclinical data are essential to avoiding harmful clinical outcomes and that policy should be evidence-based, predictable, and transparent. Critics sometimes argue that the regulatory process lacks flexibility or that some requirements impose unnecessary costs; supporters counter that consistent standards sustain public trust and long-term industry viability.

Controversies around nonclinical testing often center on animal welfare, data interpretation, and the pace of methodological change. Some critics advocate rapid adoption of alternatives, arguing that animal use is ethically indefensible and scientifically inconclusive. Supporters argue that current substitutes, while improving, do not yet fully replicate human physiology for all endpoints, and eliminating or bypassing established nonclinical steps could raise patient risk and undermine regulatory credibility. In this view, responsible reform proceeds through incremental improvements—strengthening GLP-compliant data, expanding validated nonanimal methods, and increasing transparency in how decisions are made—rather than sweeping, unproven replacements. When discussions turn to broader cultural critiques of science and regulation, a pragmatic stance emphasizes patient safety, effective risk management, and competitive innovation as compatible goals rather than mutually exclusive ones.

Economic and policy implications

Nonclinical development is a major driver of research cost and project timelines. Efficient, predictable nonclinical programs can shorten the path to clinical testing, attracting investment and enabling faster delivery of new therapies to patients who need them. Conversely, overly rigid or duplicative requirements can raise costs and deter promising programs, particularly for smaller companies and startups. A policy environment that emphasizes risk-based regulation, harmonization across markets, and continued investment in validated alternative methods is seen by market-oriented observers as essential to maintaining global competitiveness while safeguarding public health. See economic policy and regulation.