Scientist examining drug discovery vial with data matrix reflection, symbolizing reliable bioanalysis.

Drug Discovery's Quality Check: How Incurred Sample Reanalysis Boosts Confidence

Lena Kashyap monogram
Lena Kashyap in Health & Wellness October 2026 • 4 min read.

"Ensuring reliable data in early-stage drug development with Incurred Sample Reanalysis (ISR) – a key strategy for robust bioanalysis and confident decision-making."


In the high-stakes world of drug discovery, generating accurate and reliable data is paramount. Bioanalysis, the process of measuring drug concentrations in biological samples, plays a crucial role in evaluating potential drug candidates. This data drives critical decisions, guiding researchers toward promising leads and away from dead ends.

However, the fast-paced nature of early-stage drug discovery can sometimes compromise the thoroughness of quality control. That's where Incurred Sample Reanalysis (ISR) comes in. ISR is a process of re-analyzing a subset of samples to verify the accuracy and reproducibility of the original results. This confirmation step helps ensure that the data used to make key decisions is robust and reliable.

This article will delve into the application of ISR in drug discovery bioanalysis. We'll explore how this strategy can be implemented to enhance the quality of data, optimize drug development, and ultimately increase the chances of success. We'll also look at how to introduce ISR strategy that enforces adequacy of controls in sampling, processing errors including differences in extraction recovery.

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Data Quality in Discovery Bioanalysis

Bioanalysis quantifies and characterizes drugs, metabolites, and biomarkers, making it a specialized branch of analytical chemistry relevant to drug discovery. Discovery bioanalysis requires a reasonable level of confidence in data quality for drug-discovery decisions. Recent developments include software that helps field experts preprocess bioanalytical data for machine-learning-based drug applications, while modern workflows increasingly capture raw analytical data directly into centralized systems and automate data handling.

Validation and Its Limits

Bioanalytical method development defines a method's design, operating conditions, limitations, and suitability for its intended purpose. One review reports that assay methods typically require a relative standard deviation of no more than 2.0% between two data sets. The FDA cautions that validation data supplied by a diagnostic-kit manufacturer may not establish that the kit method is reliable for drug-development purposes. Poorly developed or inadequately validated methods can compromise data integrity, delay regulatory submissions, and increase costs.

From Drug Measurement to Modern Discovery

Historically, measuring drugs in biological fluids was initially driven by the need to identify possible overdosing. The development of pharmacokinetics as a science in the 1930s further increased the need to measure drug levels in biological fluids. Bioanalysis later became part of a drug-development process that one review describes as costing around $1 billion and taking approximately 10 years for a new drug to reach the marketplace.

ISR: A Strategy for Reliable Bioanalysis

Scientist examining drug discovery vial with data matrix reflection, symbolizing reliable bioanalysis.

Incurred Sample Reanalysis (ISR) is not new. However, its usage in early drug discovery is. Traditionally, ISR is a regulated bioanalysis requirement. It is used to assess bioanalysis only after completion of every study when the drug enters drug development. ISR is now being proposed and used earlier in the development stage.

The primary goal of ISR is to confirm the reliability of the original bioanalytical data. By re-analyzing a portion of the samples, researchers can identify any potential issues with the analytical method, sample handling, or data processing. This helps to ensure that the data is accurate and can be confidently used for decision-making.

  • Enhanced Data Quality: ISR helps identify and correct errors, leading to more reliable pharmacokinetic data.
  • Informed Decision-Making: Confident data supports better decisions about which drug candidates to advance.
  • Optimized Development: Early detection of issues can save time and resources in the long run.
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Bioanalysis Across the Development Path

Recent reviews describe drug development as long, expensive, and risky, pressures that have contributed to changes in the broader research-and-development model. Bioanalytical studies and method development support most stages of drug development, including preclinical testing. Bioanalytical techniques also help evaluate pharmacokinetic, pharmacodynamic, and toxicity data and can inform regulatory decisions.

Where Confidence Can Break Down

Bioanalytical conclusions depend on the quality and suitability of the methods used to produce them. In practice, inadequate method development, validation limitations, or inconsistent analytical results can weaken confidence in drug-development decisions. These risks mean that bioanalytical findings should be interpreted with attention to method performance, study context, and the possibility of technical failure.

Discovery and Regulated Bioanalysis

Discovery and regulated bioanalysis differ in their compliance requirements and in when they are used during drug development. Discovery bioanalysis supports early investigation, while regulated bioanalysis is associated with more formal compliance needs. Alternative drug-discovery models can also provide translational tools for studying safety and efficacy, including matched normal-versus-tumor comparisons for monoclonal-antibody off-target profiling.

To implement an effective ISR strategy, a few key considerations must be addressed. These include: sample selection, acceptance criteria, and frequency of analysis. It's important to select samples that represent the full range of concentrations and time points, as well as samples that are critical for decision-making. Setting appropriate acceptance criteria for the re-analysis results is also crucial. Lastly, deciding how often to perform ISR will depend on the stage of drug discovery and the risk associated with the project.

The Future of ISR in Drug Discovery

As drug discovery continues to evolve, the importance of reliable data will only increase. ISR is a valuable tool for ensuring the quality of bioanalytical data and supporting informed decision-making. By embracing ISR, researchers can increase their confidence in the data, accelerate the drug development process, and ultimately improve the chances of success.

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Expertise and Tissue-Level Evidence

Discovery bioanalysis focuses on identifying and quantifying drugs, metabolites, and proteins in biological systems, and it plays a crucial role in drug development. Tissue analysis is described as a diverse, active subfield of bioanalysis and as a powerful tool for drug discovery and development. Expert Opinion on Drug Discovery is a monthly peer-reviewed medical journal that publishes review articles on novel technologies involved in drug discovery.

Efficiency, Modalities, and Market Growth

IQVIA's Bioanalysis 2026 perspective identifies efficiency gains, adaptation to new drug modalities, and support for innovative disease treatments as central themes in the next wave of bioanalysis. Market projections differ across sources: one forecast places the discovery bioanalysis market at a 14.4% compound annual growth rate from 2026 through 2033. Another projects bioanalytical testing services growing from USD 4.94 billion in 2025 to USD 10.90 billion by 2035, while a separate forecast estimates growth from USD 5.25 billion in 2026 to USD 18.66 billion by 2035.

Quality Control Across Complex Systems

Structural, bioanalytical, characterization, and quality-control studies are described as critical to successful drug development. Discovery bioanalysis also faces current challenges and emerging opportunities in the quantification of biologics by liquid chromatography-mass spectrometry. These pressures reflect the need to maintain reliable analytical characterization as drug programs and molecular formats become more complex.

Evidence Beyond the Trial

An oncology case-study source reports that real-world evidence can provide timely, informative data that complements clinical-trial findings. Drug-development case studies also document work addressing client biosimulation and regulatory-science issues. Other clinical research case studies emphasize expert insights and real-world perspectives from a global contract research organization and scientific partner, while an AI case study discusses how artificial intelligence can aid pharmaceutical drug design and discovery.

While implementing an ISR strategy adds workload, periodically planned ISR program, every 6 months, should enable a continuous review and improvement of laboratory related practices which in turn will have positive impact on the data driven decisions during drug discovery process.

The individual bioanalytical laboratory can draw an algorithm for selecting NCEs as well as the acceptance of the ISR data. For example, the regulatory guidance for developmental candidates recommends an ISR acceptance target for 67% of the repeat sample values to be contained within 20% of the original values (USFDA, 2018; EMA 2011).

ISR results against sample and dataset baselines

  • 40 samplesIn simulations using methods with 0% bias and 10% CV, 40 samples allowed correct decision-making.[1]
  • 97.7% and 95.0% percentThe reported ISR pass rates were 97.7% for ulotaront and 95.0% for SEP-383103.[2]
  • 26 studiesAcross more than 1,000 pharmacokinetic endpoints, 26 studies failed their predefined ISR criteria.[3]
  • three partial and two full datasetsThose failures resulted in rejection of three partial and two full datasets.[3]

What ISR results reveal about discovery data

Because study samples can behave differently from the spiked blank-matrix standards and quality controls used in validation, ISR can expose differences that those controls may not reflect.[4]

ISR failures were not merely numerical disagreements: reported investigations identified assay variability, an unsuitable assay range, sample homogeneity, instability, and preanalytic contamination as possible root causes.[3]

How ISR findings compare across settings

The simulation result links correct decisions to a defined sample count and to methods described as accurate and precise, whereas laboratory and study reports describe observed ISR outcomes in varied settings.[1] [2] [5]

The guideline says an individual result far from its original value, such as a flyer, should not by itself trigger reanalysis or investigation; this distinguishes an isolated discrepant value from evidence of a broader dataset problem.[4]

What ISR evidence still leaves unresolved

  • How much additional value ISR provides in intermediate development studies remains contested, rather than settled by the reported experience.[3]
  • Which sampling time points best capture ISR performance across programs is not resolved by the fact that assessments included samples around absorption, distribution, and elimination while timing varied.[6]
  • Reliable pharmacokinetic and toxicokinetic results matter for decisions, but the evidence here does not establish how ISR outcomes translate into the accuracy of those decisions across discovery and development settings.[1] [6]

Sources

  1. Incurred Sample Reanalysis: Time to Change the Sample Size Calculation? (pmc.ncbi.nlm.nih.gov)
  2. Evaluation of OCT2‐mediated drug–drug interactions between ulotaront and metformin in subjects with schizophrenia (pmc.ncbi.nlm.nih.gov)
  3. Incurred sample reanalysis at GSK: what have we learned? (pubmed.ncbi.nlm.nih.gov)
  4. ICH guideline M10 on bioanalytical method validation and study sample analysis_Step 5 (www.ema.europa.eu)
  5. Identifying trends and developing solutions for incurred sample reanalysis failure investigations in a bioanalytical CRO. (pubmed.ncbi.nlm.nih.gov)
  6. Incurred sample reanalysis in drug discovery bioanalysis. (analyticalsciencejournals.onlinelibrary.wiley.com)

About this Article -

Written with AI assistance from published research, and reviewed by the Mystum team. See our About page for more information.

Everything You Need To Know

1

What is Incurred Sample Reanalysis and how does it work in drug discovery?

Incurred Sample Reanalysis, or ISR, is a method of re-analyzing a subset of biological samples. This is done to verify the accuracy and reproducibility of the initial results obtained during bioanalysis. This process helps to confirm the robustness and reliability of the data, ensuring that critical decisions in drug discovery are based on sound evidence.

2

What are the primary advantages of using Incurred Sample Reanalysis in early-stage drug development?

The main benefits of using Incurred Sample Reanalysis include enhanced data quality, which involves identifying and correcting errors in pharmacokinetic data. It also supports informed decision-making, which allows for better choices about which drug candidates to advance. Furthermore, it optimizes the overall development process by detecting issues early, which can save time and resources in the long run. It also enforces adequacy of controls in sampling, processing errors including differences in extraction recovery.

3

What are the key considerations for implementing an effective Incurred Sample Reanalysis strategy?

To implement Incurred Sample Reanalysis effectively, there are three key considerations to address. First is sample selection, where it's important to choose samples that represent the full range of concentrations and time points, focusing on those critical for decision-making. Next is acceptance criteria, which involves setting appropriate standards for the re-analysis results. Lastly is frequency of analysis, where deciding how often to perform ISR will depend on the stage of drug discovery and the risk associated with the project.

4

When is Incurred Sample Reanalysis traditionally used, and how is its application evolving in drug discovery?

Incurred Sample Reanalysis is usually used as a regulated bioanalysis requirement to assess bioanalysis after the completion of every study, when the drug enters drug development. Now, Incurred Sample Reanalysis is being proposed for use much earlier in the drug development stage. This allows for the confirmation of method reproducibility during the early stages of drug development.

5

What is the future of Incurred Sample Reanalysis in drug discovery, and what impact does it have on the overall success of the process?

As drug discovery progresses, the importance of reliable data will only increase. Incurred Sample Reanalysis serves as a valuable tool for ensuring the quality of bioanalytical data, and supports well-informed decision-making. By using Incurred Sample Reanalysis, researchers can be more confident in their data, accelerate the drug development timeline, and increase the probability of success. Without ISR, errors in early bioanalysis could lead to the misidentification of promising drug candidates or the wasteful pursuit of ineffective ones, resulting in significant financial and time costs.

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