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Bioanalytical labs play a crucial role in drug development, providing essential data to answer fundamental questions like “Does it work?” and “Is it safe?” The speed at which scientists can make informed decisions directly impacts the pace of bringing new drugs to market. To meet this challenge, labs are turning to digital solutions that streamline operations and improve data quality.

Unlocking the Power of Data

One of the key assets in modern bioanalytical labs is data. Smart data management can save time, reduce waste, and provide reliable answers quickly. However, in many labs, data is scattered across various systems, including paper notebooks, and spreadsheets. This fragmented approach makes it challenging to leverage data efficiently, leading to missed opportunities and inefficiencies.

With the rise of connected instruments and advanced analytical instruments like ELNs, LIMSs, labs can now integrate their data into a central backbone. This integration allows for streamlined operations, reduced human errors, and improved data accessibility. By centralizing data, labs can create user-friendly reports, and workflows, enabling scientists to make faster, more informed decision

Power of ELN, LIMS and LES for Bioanalysis

The digital transformation of bioanalytical labs is greatly facilitated by the use of Laboratory Information Management Systems (LIMS), Electronic Lab Notebooks (ELN), and Laboratory Execution Systems (LES). These systems play crucial roles in streamlining operations, improving data quality, and enhancing decision-making processes.

LIMS (Laboratory Information Management Systems):

LIMS are central to the implementation of a digital strategy in bioanalytical labs. They provide a structured framework for managing sample information throughout its lifecycle. By tracking sample details from login to disposition, LIMS ensure that data is captured accurately and consistently. This centralized approach to data management improves data integrity and accessibility, enabling scientists to make informed decisions more efficiently.

LIMS play a key role in integrating data from various sources, such as instruments, assays, and experiments. By providing a unified platform for data storage and management, LIMS enable labs to streamline operations and reduce manual errors. This integration also facilitates compliance with regulatory requirements, as data can be easily audited and traced back to its source.

Overall, LIMS contribute significantly to the efficiency and effectiveness of bioanalytical labs, enabling them to leverage data more effectively and make informed decisions.

ELN (Electronic Lab Notebooks):

ELNs are another essential tool in the digital transformation of bioanalytical labs. They provide a digital platform for recording and managing experimental data, replacing traditional paper lab notebooks. ELNs offer several advantages over paper notebooks, including the ability to standardize workflows, automate data entry, and facilitate collaboration among scientists.

One of the key benefits of ELNs is their ability to standardize experimental workflows. By providing templates for recording experimental details, ELNs ensure that data is captured consistently and accurately. This standardization not only improves data quality but also makes it easier to search and analyze data.

ELNs also facilitate collaboration among scientists by providing a central platform for sharing and accessing experimental data. This collaborative approach to data management enables scientists to work more efficiently and effectively, leading to faster decision-making and better outcomes.

LES (Laboratory Execution Systems):

LES are specialized systems designed to automate and enforce procedural steps in the laboratory. In the context of bioanalytical labs, LES play a crucial role in ensuring that experiments are conducted consistently and according to standard operating procedures (SOPs).

One of the key advantages of LES is their ability to enforce procedural execution during testing. By encapsulating SOPs into software, LES ensure that each step of the testing process is recorded and completed before moving on to the next step. This not only improves data quality but also reduces the risk of errors and deviations from protocol.

LES also facilitate real-time monitoring of experiments, allowing scientists to make informed decisions based on up-to-date data. This real-time feedback loop enables labs to respond quickly to changing conditions and optimize experimental workflows for better results

Refining Bioanalytical Labs: Unifying Digital Solutions for Efficiency, Quality, and Innovation

1. Deliver a Platform-Based yet Personalized Laboratory Experience
While personalization of laboratory technologies can be beneficial in the short term, it often leads to information silos and challenges in information exchange. A platform-based approach, on the other hand, allows labs to leverage integrated modules aligned with standard enterprise-wide R&D terminologies and capabilities. This approach, facilitated by tools like LIMS and ELNs, enables better-quality study data generation and enhances collaboration among researchers. By adopting harmonized approaches across sites, labs can achieve enhanced visibility, real-time tracking of experiment statuses, and improved cross-experimental insights.

2. Leverage Digital Lab Tools to Unlock Operational Efficiency & Cost Savings
Digital lab technologies such as LIMS, ELNs, and quality management systems offer significant operational efficiencies and cost-saving opportunities. By retiring legacy systems, eliminating redundant data entry, and building audit trails, labs can streamline workflows, ensure data accuracy, and enhance compliance with regulatory requirements. Additionally, these technologies reduce employee time spent on manual tasks and enable real-time tracking of project workloads, leading to substantial time savings per employee.

3. Drive Enhanced Data Reproducibility & Data Analysis to Create Commercial Value
Data reproducibility is a critical challenge in bioanalytical labs, leading to wasted time, decreased resources, and lower scientific output. Digital platforms that enhance data quality and increase statistical power can address this challenge. By standardizing higher-quality data, labs can increase reproducibility and improve experimental performance. Furthermore, leveraging data analytics tools can help labs extract additional value from their data, accelerating the discovery of new indications and molecules.

Veeda’s Integration of LIMS, ELN, and LES Solutions

Veeda’s Bioanalysis solution integrates Laboratory Information Management System (LIMS), Electronic Laboratory Notebook (ELN), and Laboratory Execution System (LES) functionalities to optimize our bioanalytical lab operations. This integrated approach for bioanalytical studies by providing advanced data management, analysis, and automation tools in a single, cohesive system.

LIMS centralizes sample tracking and data management, ensuring traceability and compliance with regulatory standards. Meanwhile, ELN digitizes experimental data, improving collaboration and reducing manual errors. The LES further enhances our workflows by automating processes and enforcing SOPs, ensuring consistency and quality in our operations. This integration enhances our bioanalytical procedures into efficient, reliable testing methods, where we leveraging connected instruments and intelligent data management capabilities to consistently improve our deliverable outcomes.

Reference Articles:


Pharmacodynamic (PD) biomarkers indicate how a drug affects its target, like a receptor triggering a signalling cascade. They reflect the drug’s impact on the body’s biological or physiological functions. Unlike pharmacokinetics, which focuses on how the body processes a drug, pharmacodynamics explores its effects and mechanisms. These markers are vital in clinical trials, helping assess a drug’s efficacy, safety, and optimal dosage, and in individualizing treatments. They’re crucial in drug development, aiding researchers and healthcare pros in understanding a drug’s interactions and suitability for its intended use. Developing New Chemical Entities (NCE) involves discovering, designing, and synthesizing novel compounds for therapy. Bioanalysis, quantitatively measuring drugs and their metabolites in biological samples, is key in NCE development.

Challenges & Considerations

Factors Challenges Mitigations
Analytical Method Development and Validation
Developing and validating robust bioanalytical methods to quantitate the NCE and its metabolites in complex biological matrices Rigorously following regulatory guidelines, conducting thorough method validation, and adapting methods as needed during the development process
Bio matrix Interference, Matrix Standardization, Sensitivity and Specificity


Biological samples like blood or urine might have interfering substances affecting accurate drug measurement. Methods must detect low concentrations and differentiate the drug from other components, while individual differences impact consistency Efficient sample preparation using surrogate or diverse matrices, optimizing extraction protocols with advanced tools for precision & employing matrix standardization to address inter-individual variability in analysis
Automation and Throughput with Emerging Technologies Maintaining accuracy while meeting high throughput needs. Adopting cutting-edge bioanalytical tech for large molecules, prioritizing contamination control, and addressing ethical considerations with minimal sample volume Automating processes, streamlining workflows for efficiency and staying updated on new tech; assess their relevance in NCE development with hybrid methods like LBA-MS
Integration of Biomarkers
Incorporating biomarkers into bioanalytical strategies to provide insights into pharmacodynamics
Exploring and validating biomarkers that align with the pharmacological effects of the NCE


Strategies for PD Biomarker Quantitation

Quantifying Pharmacodynamic (PD) biomarkers in bioanalysis involves careful planning and execution to ensure accurate and reliable measurement of the biological responses to a drug. Here are the strategies concerning requirements and rationale for PD biomarker quantitation in bioanalysis.

Requirements Strategies Rationale
Biomarker Selection and Validation Choosing PD biomarkers that are relevant, specific, and validated to reflect the pharmacological effects of the drug Selection based on a strong scientific rationale enhances the likelihood of meaningful results
Sample Collection and Processing Establishing standardized procedures for sample collection and processing to minimize variability Considering the choice of biological matrices, collection timing, and sample storage conditions
Calibration Standards and Quality Control Samples Preparation of calibration standards with known concentrations of the PD biomarker and including quality control samples Calibration curves ensure accurate quantitation, while quality control samples assess the precision and accuracy of the assay
Internal Standards Incorporating internal standards into the assay for normalization and to correct for variations Internal standards help account for analytical variability and matrix effects
Validation of Bioanalytical Methods Rigorously validating bioanalytical methods & following regulatory guidelines Validate for selectivity, sensitivity, precision, accuracy, linearity, and robustness
Use of Stable Isotope-Labeled Internal Standards Employing stable isotope-labelled internal standards for accurate quantitation Stable isotope-labelled standards closely mimic the analyte’s behaviour, enhancing precision and accuracy. In the absence of an isotope-labelled internal standard, an analogue IS with similar characteristics can be selected
Automation and High-Throughput Techniques Implementation, automation, and high-throughput techniques for increased efficiency Automation reduces human error, and high-throughput methods are beneficial in large-scale studies
Matrix Effects and Standardization Addressing matrix effects by standardizing matrices or using matrix-matched standards Matrix effects can impact accuracy, so careful consideration of matrix standardization is crucial


Veeda’s Capabilities & Approach for Novel Drug Development Program

Bioanalysis is a vital part of drug development, focusing on accurately measuring drugs and their by-products in biological samples. A successful bioanalysis strategy involves method development, validation, and application in clinical studies.

  • At Veeda, method development involves extensive research, considering various factors like drug properties, dose, linearity range, extraction protocols, chromatography, and equipment. Method validation includes experiments ensuring compliance with regulations, such as selectivity, accuracy, precision, sensitivity, matrix effects, and stability studies. In clinical sample analysis, it’s crucial for determining drug levels in biological samples. Incurred sample reanalysis validates reported sample analyte concentrations, ensuring reliability
  • Employing emerging technologies like LC-MS/MS machines, ICP-OES, LIMS, and BSL-2 labs enhances our capabilities. Quality management systems (QMS) established protocols ensuring consistent quality standards, customer satisfaction, and regulatory compliance
  • Data analysis and statistical approaches at Veeda derive meaningful insights from experimental results, ensuring their reliability and validity
  • Regulatory compliance involves adherence to industry-specific laws, guidelines, and standards
  • Cross-validation with clinical endpoints ensures alignment between laboratory analyses and clinical outcomes, establishing correlations between measured biomarkers/drug concentrations and therapeutic effects/safety outcomes

Our Expertise in PD Biomarker Method Development & Validation

Biomarkers Veeda’s Expertise
Alpha-1-acid glycoprotein Determination of, α1 Acid Glycoprotein (AAG) of in K3EDTA Human Plasma by Using LC-UV with linearity range of 300µg/mL to 5000µg/mL


Coproporphyrin I Determination of Coproporphyrin I in altered and Unaltered plasma by using LC-ESI-MS/MS, with linearity range of 50pg/mL to 5000pg/mL
Symmetric Dimethylarginine (SDMA) Determination of SDMA in stripped and un-stripped plasma by using LC-ESI-MS/MS, with linearity range of 2.00ng/mL to 4000ng/mL
Uridine Determination of Uridine and L-Dihydroorotic acid(L-DHO) in altered and unaltered, Plasma by using LC-ESI-MS/MS with linearity range of 30ng/ml to 30000ng/ml for Uridine and 3.0ng/mL to 3000ng/mL for LDHO
C-peptide Determination of C-Peptide in human serum by Using ECLIA Method on Immuno-assay Analyzer Cobas e 411


Bioanalysis is pivotal in identifying, measuring, and characterizing Pharmacodynamic (PD) markers, which indicate a drug’s biological effects in an organism. Its role involves:

  • Identification: Using techniques like mass spectrometry, immunoassays, and chromatography to screen and identify potential PD markers
  • Quantification: Developing precise methods to measure PD markers accurately
  • PK/PD Modelling: Integrating bioanalytical data into models for predictive insights on drug concentration and PD marker levels
  • Dose-Response Assessment: Analyzing concentration-response relationships to establish dose-response curves
  • Early Phase Development: Using bioanalytical data to guide decisions about dosing, further development, and safety concerns
  • Safety Assessment: Identifying and measuring biomarkers that signal potential safety issues during drug development


  1. Abbas M, Alossaimi MA, Altamimi AS, Alajaji M, Watson DG, Shah SI, Shah Y, Anwar MS. Determination of α1-acid glycoprotein (AGP) concentration by HPLC in patients following local infiltration analgesia for primary total hip arthroplasty and its relation to ropivacaine (total and unbound). Frontiers in Pharmacology. 2023;14
  2. Kandoussi H, Zeng J, Shah K, Paterson P, Santockyte R, Kadiyala P, Shen H, Shipkova P, Langish R, Burrrell R, Easter J. UHPLC–MS/MS bioanalysis of human plasma coproporphyrins as potential biomarkers for organic anion-transporting polypeptide-mediated drug interactions. Bioanalysis. 2018 May;10(9):633-44
  3. Shin S, Fung SM, Mohan S, Fung HL. Simultaneous bioanalysis of l-arginine, l-citrulline, and dimethylarginines by LC–MS/MS. Journal of Chromatography B. 2011 Mar 1;879(7-8):467-74
  4. Yin F, Ling Y, Martin J, Narayanaswamy R, McIntosh L, Li F, Liu G. Quantitation of uridine and L-dihydroorotic acid in human plasma by LC–MS/MS using a surrogate matrix approach. Journal of Pharmaceutical and Biomedical Analysis. 2021 Jan 5;192:113669
  5. US Food and Drug Administration; U.S. Department of Health and HumanServices; Food and Drug Administration; Center for Drug Evaluation and Research (CDER); Center for Veterinary Medicine (CVM). Bioanalytical Method Validation: Guidance for Industry; U.S. Department of Health and Human Services, Food and Drug Administration: Silver Spring, MD, 2018


Chronic Obstructive Pulmonary Disease (COPD) and Asthma are significant respiratory conditions that affect millions worldwide. In 2019, COPD accounted for 3.3 million deaths and 74.4 million disability-adjusted life years (DALYs), with a global prevalence of 212.3 million cases. Meanwhile, the prevalence of Asthma has been rising due to increased life expectancy and changing demographics. Additionally, the overlap of Asthma and COPD cases has become more frequent, presenting unique challenges in diagnosis and treatment.

Current Treatment Landscape

  1. Bronchodilators: The use of both short-acting inhaled bronchodilators (albuterol and ipratropium) as rescue therapy and long-acting bronchodilators (LABAs and LAMAs) has become common. Several new bronchodilators are in development, showing promise for future therapies.
  2. Muscarinic Antagonist–β2-Agonists (MABAs): MABAs are under clinical trials, though challenges exist in balancing their LABA and LAMA activity.
  3. New Corticosteroids: Fluticasone furoate, a once-daily inhaled corticosteroid (ICS) in combination with vilanterol, offers a new option. However, safety concerns related to corticosteroids remain.
  4. Phosphodiesterase Inhibitors: Roflumilast is currently marketed as an anti- inflammatory treatment in COPD, but its narrow therapeutic window limits its use.
  5. Kinase Inhibitors: Some kinase inhibitors have shown promise in COPD and Asthma models, but challenges in specificity and side effects require further research.
  6. Mediator Antagonists: CRTh2 antagonists, cytokine inhibitors, and protease inhibitors have been widely used in Asthma treatment, but their efficacy varies.
  7. Antioxidants: While antioxidants like N-acetylcysteine and sulforaphane have been explored, their efficacy remains limited.

Challenges and Suggested Approaches

Researchers face challenges in developing novel drugs for Asthma and COPD, including limited investment by pharmaceutical companies, lack of funding for basic research, and a scarcity of helpful biomarkers. To overcome these hurdles, identifying new therapeutic targets and biomarkers is crucial for better patient selection and long-term therapy monitoring.

New approaches in COPD and Asthma treatment include:

  • Reversing Corticosteroid Resistance: Finding solutions to the challenge of corticosteroid resistance in patients.
  • Resolving Inflammation and Aberrant Repair: Addressing inflammation and tissue repair dysregulation.
  • Decelerating Aging: Focusing on strategies to mitigate the impact of aging on disease progression.

Biomarker-Driven Trial Designs

Biomarker-driven trial designs are transforming the landscape of COPD and Asthma treatments, offering a more precise and personalized approach to patient care. These innovative trial designs focus on specific biomarkers that play a crucial role in understanding the underlying mechanisms of these respiratory conditions and predicting treatment responses.

In COPD, eosinophilic inflammation is a key biomarker that helps identify patients who are more likely to respond favorably to inhaled corticosteroids (ICS) and certain biologic therapies targeting type 2 inflammation. Conversely, in non-type 2 inflammation, neutrophilia becomes a significant biomarker, guiding clinicians to explore alternative treatment strategies due to a reduced response to ICS.

For Asthma, fractional exhaled nitric oxide (FeNO) levels serve as a valuable biomarker for type 2 inflammation. Elevated FeNO levels are associated with a higher likelihood of responding well to ICS and specific biologic agents like anti-IgE and anti-IL-4R treatments. Additionally, IgE levels can indicate atopy and predict better responses to ICS and anti-IgE treatments.

Periostin emerges as a promising biomarker in both COPD and Asthma. It is associated with type 2 inflammation and airway remodeling, making it a potential indicator of treatment response to anti-IL-13 therapies in Asthmatic individuals with high periostin levels.

Summary of Clinical Trial Findings

Biomarkers are essential tools in guiding treatment decisions and assessing therapy response for Asthma and COPD. These biomarkers help in patient stratification, identifying subgroups likely to respond to specific therapies, and reducing the risk of adverse effects.

Contract Research Organizations (CROs) play a crucial role in advancing biomarker-driven research. They possess specialized expertise in biomarker discovery, validation, and analysis, accelerating the translation of research findings into clinical applications


In conclusion, COPD and Asthma present significant global health challenges, affecting millions of people and causing substantial morbidity and mortality. The current treatment landscape has seen advancements, but unmet needs persist. Biomarkers offer promising opportunities fo personalized treatments, while CROs play a crucial role in advancing research and developmen efforts. To address the challenges, increased investment in respiratory medicine research is essential. By fostering collaboration and innovation among stakeholders, we can strive toward better management and improved outcomes for patients living with COPD and Asthma ultimately enhancing their quality of life.


Disease Overview :

Global Scenario :

In developed nations, the prevalence of Chronic Myeloid Leukaemia (CML) is primarily concentrated among the elderly population, typically aged 60 and above. In contrast, in developing nations, the diagnosis of the disease occurs approximately ten years earlier, impacting individuals in their 50. It is the most common type of blood cancer.

Indian Scenario :

Chronic Myeloid Leukaemia (CML) is a clonal myeloproliferative disorder of a pluripotent stem cell. CML is the commonest adult leukaemia in India and the annual incidence ranges from 0.8– 2.2/100,000 population in males and 0.6– 1.6/100,000 population in females in India.

Out of the 250 CML Trials in active stage, 123 CML Trials worldwide are Phase II trials. 38 CML Trials are exclusively industry funded or are in collaboration with academia and small biopharmaceutical companies.

Why there is need to conduct CML Trials?

CML is the world’s first cancer with specific genotype knowledge, which led to a rationally therapeutic schedule. Imatinib, a tyrosine kinase inhibitor (TKI), was approved by the FDA to treat CML in 2001. The discovery of the TKI-based treatment, changed the CML disease status from a lethal disease to a chronic disease, especially for patients in the chronic phase. There has been an apparent improvement in the survival of CML patients in high-income countries like the United States, France, and Japan. The disease burden of CML distinctly varies in different countries due to diverse opportunities for early-stage screening, novel drugs and medical resources.

Prevailing trends in CML Clinical Trials

Targeted Therapies :

The development of targeted therapies, such as tyrosine kinase inhibitors (TKIs), has been a significant trend in CML clinical trials. TKIs, such as Imatinib, Dasatinib, and Nilotinib, have revolutionized the treatment of CML by specifically targeting the abnormal BCR-ABL protein responsible for the disease.

Treatment-Free Remission (TFR) :

TFR is a growing area of interest in CML clinical trials. It focuses on the possibility of discontinuing TKI treatment in patients who achieve deep molecular responses, aiming to maintain disease control without the need for ongoing therapy.

Combination Therapies :

Investigating the effectiveness of combining different TKIs or combining TKIs with other agents is an ongoing trend in CML clinical trials. Combinations may enhance treatment response, overcome drug resistance, and improve long-term outcomes for patients.

History of Targeted Therapy for CML Trials


Key Challenges and Considerations: Operational & Clinical

The challenges in CML clinical trials are based on the four phases as mentioned below:

  • Chronic Phase
  • Accelerated Phase
  • Accelerated Phase with Patients with NO prior treatment
  • Accelerated Phase with Patients with prior treatment

CML clinical trials across different phases present obstacles for CROs in their operational and clinical activities. These challenges include communication and coordination with sponsors, complex protocols, site monitoring difficulties, patient population identification, geriatric research, study cost management, staff training, and utilization of technology-enabled platforms.

*Below is the chart that shows the impact of these above mentioned challenges with respect to CML Phases for a CRO:

*3/4 of the graph is blue: classified as a major impact, 1/4 of the graph is blue: classified as minor impact, 1/2 of the graph is blue: classified as neutral

Veeda Oncology

In conclusion, CML clinical trials have witnessed significant progress, aided by the expertise of Indian CROs. With our proficiency in managing protocol complexities, addressing the unique requirements of the geriatric population, and optimizing costs, Veeda stands ready to accelerate your upcoming CML trial. We remain dedicated to offering exceptional support to sponsors engaged in CML research. By leveraging our extensive knowledge, sponsors can expect a seamless trial experience, adherence to regulatory requirements, and the generation of robust data. Contact us today to know more about Veeda’s CML trial services.



Development and Execution of In Vivo Bioassays

Bioassays are involved in each stage of drug discovery, starting from Target Identification until discovering the Lead compound. Bioassays provide valuable information which displays the therapeutic potency of a drug under investigation.

The data generated during bioassay also plays a vital role in drug development and quality control of finished biological products. Properly designed Bioassays help in assessing the biological effect, activity, signal transduction process, and receptor binding ability of drug product or biologic on a biological target (proteins) when compared to a reference or standard over a suitable biological system.

The pharmaceutical and biotech companies involved in drug discovery and development are continuously challenged with developing biologically relevant assays for the analysis of multiple potential mechanisms.

The process involves the use of quality critical reagents, use of specific cell lines, and purified test drug and reference drug products which at times may become a constraint. Most of these activities require sufficient time, which may become a limiting factor to biopharma manufacturers.

It is worth outsourcing activities to reputed CRO service providers to save time in developmental efforts and also to have an unbiased opinion on the functional activities of the drug product.

Veeda Group has qualified and experienced scientists to design, develop, execute, and validate the Bioassays for companies and provides premier bioassay services (in vitro and in vivo) that generate meaningful data to support pharmaceutical and biotech companies in their drug discovery and development journey.

Veeda Group’s Experience in Development and Execution of Bioassays include:

  • Plaque Reduction Neutralization Test (PRNT assay)
  • In Vitro Skin Sensitization Human Cell Line Activation test (h-CLAT assay)
  • Nab Assay
  • Assay Development (Pharmacodynamics, Pharmacokinetics, Immunogenicity, and Biomarker Assessment)
  • In Vivo Bioassays for drug molecules like Luteinizing Hormone, Epoetin, HCG, Recombinant FSH, β-HCG, and Insulin.
  • ADCC assay for biosimilars and different other assays like Ex Vivo assay, Cell-based assay, Receptor Binding Assay, Cytokine Release Assay, and ADA assay.

Veeda Group provides Integrated Discovery, Development & Regulatory Services with its multiple technology platforms:

  • Exploratory toxicology studies
  • Regulatory toxicology studies
  • In vitro Bioassays
  • Ex vivo Bioassays

The group also has the experience to handle a diverse range of Biotherapeutics like Therapeutic Monoclonal Antibodies, Insulin & Insulin Analogues, Cytokines, Low Molecular Weight Heparins, Biosimilars, Hormones & Biomarkers.

Veeda group has demonstrated capabilities to develop recombinant proteins such as non-glycosylated proteins and glycoproteins derived from either bacterial or mammalian host expression systems.

Bioassays in Preclinical Drug Development

Biological assays or bioassays are essential tools in preclinical drug development. Preclinical bioassays can be in vivo, ex vivo, and in vitro.

In vivo bioassays provide a more realistic and predictive measure of the functional effects of tests with reference drug products or standard material of defined potency, along with the application of statistical tools, study-specific lab techniques, and adherence to the well-designed study protocol.

These assays capture the complexity of target engagement, metabolism, and pharmacokinetics of novel drugs better than in vitro bioassays.

The most commonly used experimental mammals in in vivo efficacy assays are mice and rats. Occasionally other species may be used depending on the sensitivity & suitability of the assays.

Development and Validation of Bioassays

Bioassays are used as a screening method to identify the signals that indicate desired biological activity from a set of compounds. In general, two different types of signals can be generated by a bioassay, a linear dose-response and a sigmoidal (S-shaped) dose-response.

Since one solution does not fit all bioassays, it is good to evaluate and analyze the data to develop a precise approach to carry out each bioassay.

The life cycle stages of a bioassay are divided into:

Stage 1: Method design, development, and optimization

Stage 2: Procedure performance qualification

Stage 3: Procedure performance verification (fit for purpose)

Developing a bioassay that meets regulatory requirements and gets a drug product registered is a very complex process.

Developing a bioassay includes many strategies and tactical designs like selecting the correct in vivo platform, proper method or plate design, data analysis, system/ sample sustainability strategy, method implementation, method performance, and monitoring.

There are several steps to be followed for the development and validation of bioassays, such as dose-response and curve-fitting selection, development of reference, calculation of potency, bioassay characterization, design of bioassay calculator, standardization and automation of bioassay, and finally, evaluation.

Both method development and validation of bioassays include three fundamental areas:

  1. Pre-study (Identification and Design Phase) validation
  2. In-study (Development and Production Phase) validation
  3. Cross validation or method-transfer validation

During method development, assay conditions and procedures are selected that minimize the impact of potential sources of invalidity. Coming to the statistical validation for an in vivo assay, it involves four major components:

  1. Adequate study design and data analysis method
  2. Proper randomization of animals
  3. Appropriate statistical power and sample size
  4. Adequate reproducibility across assay runs.

Parallel group design, randomized block design, repeated measures design, and crossover design are the basic types of experimental designs used in in vivo assay.

The following are the key factors that should be kept in mind while designing an in vivo assay:

  • All meaningful biological effects (pharmacologically) should be statistically significant.
  • If biologically relevant assays are not present, then a range of plausible effects can be considered.
  • The key endpoints should be well-defined before the beginning of the assay.
  • Animals should be allocated randomly in an appropriate manner to the treatment groups.
  • The dose levels should be selected appropriately. Dose and curve-fitting selection is among the most critical aspects of bioassay development. The dose is determined depending on the type of model used in the signal to fit the data. For Sigmoidal designs, a four- or five-parameter logistics (4PL or 5PL) model fits the data, whereas, for linear design, a parallel line analysis (PLA) model fits the data.

For a 4PL model, nine doses are recommended:

  1. Three doses in the lower asymptote
  2. Three doses in the upper asymptote
  3. Three doses in the linear range

In contrast, for a PLA model, a minimum of four doses is recommended. A minimum of three consecutive doses is required to plot the dose curve.

  • The selection of control groups and time points to collect samples should be optimal.
  • The design strategies should minimize variability and maximize information.

To understand the design, developments, and statistical validation of in vivo bioassay in more detail, reach out to us at One can also read the guidelines mentioned by NIH by visiting the link:

Chart Diagram of Assay Development Stage in the Drug Discovery and Development Process


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  2. Padmalayam, Ph.D., Assay development in drug discovery
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