How has flow cytometry recently contributed to the field of cellular and genetic therapeutics?
Flow cytometry is a powerful tool used in the research and development of cell and gene therapy products. With this tool, the researcher can gain valuable insight into the phenotype and function of populations of individual cells and how those cells respond to perturbations in their respective environments. In the development of cellular medicine, flow cytometry is used for the assessment of culture health, phenotypic characterization of in-process culture and final product, as well as the functional characterization, to quantify the effect of potential process changes as well as indicate the labs’ capability of making a safe and effective product on the lab bench.
Flow cytometry has been utilized for the immune monitoring of CAR-T cells. What are the next cellular targets for therapy that can be monitored with flow cytometry?
CAR-T cell characterization, such as phenotyping and functional analysis: In vitro CTL assay: CD107A Flow assay and IFN-g production.
What are some important aspects to consider when deciding on bioanalytical techniques during cell and gene therapy studies?
- Skill and Capacity Shortages: the required expertise in molecular biology or performing flow cytometry at the standards required for regulatory approval.
- Innovation and Creativity are Required
- Reagent Quality Must be Addressed. One of the key challenges facing modern bioanalytical scientists is the variability in reagent quality. For both ELISA and cell-based assay reagents, a lot of time and effort is wasted on ensuring that lot-to-lot variability does not impact the results generated over time to support a program.
How does the quantitation of cell populations aid in the development of cell and gene therapies?
According to cGMP regulations, quality is built into the design of the process and in every manufacturing step. Due to the complex nature of cell and gene therapy products, a cautiously devised list of in-process and release tests is required to provide adequate evidence of identity, safety, purity, and potency. Take CAR-T cell productions as an example, the identity of CAR-T cell products is commonly characterized by CAR surface expression. The purity of the product relies in part on specified levels of CD3+ and CAR+ T cells. Up to now, the potency of CAR-T cells is often determined by in vitro cytotoxic T lymphocyte assay or interferon-σ secretion.
What capabilities does flow cytometry bring to cell and gene therapy development that make it a favorable bioanalytical tool in the laboratory?
Just 15 years ago, an average bioanalytical lab largely relied on chromatographic methods. With the advent of mAb therapies, ligand-binding assays for immunogenicity (e.g., ELISA) became widely used. Today, cell-based assays, flow cytometry, and molecular biology-based methods, such as branch-chain DNA analysis, are important.
Flow cytometry has itself evolved to meet changing needs. Initially designed to detect cell types based on surface characteristics, flow cytometry is now combined with the detection of specific intracellular properties (intracellular flow cytometry) to characterize signaling networks at the single-cell level. Gating strategies required to identify the cell populations are developed by the bioanalytical scientist and must be implemented in a very manual process. Flow cytometry thus involves art as much as science and requires deep knowledge and understanding of the technique and the products under evaluation. One of the key workflows is centralizing the data review, and processing to a single team for a global trial can ensure consistency in the data. The European Bioanalysis Forum document on best practices for flow cytometry in a regulated environment provides invaluable guidance on traceability and comparability of data.
What are the bioanalytical challenges to implementing flow cytometry into cell and gene therapy developments?
Complicating the situation is the lack of specific regulatory guidance on cell-based assays and the use of flow cytometry for drug development applications. Guidance documents exist for chromatography and ligand-binding assays, but only a few white papers have been published on bioanalysis for cell and gene therapies. Regulators want to drive approvals of novel treatments, and in the absence of clear guidance, they will accept new methods provided that evidence shows that the treatments are robust and appropriate.
Both pharma companies and CROs must be innovative and develop techniques that will enable them to provide the required data and find solutions to new challenges — such as the cross-validation of a flow cytometry method in two laboratories — as they arise. Bioanalytical scientists working with clinicians can effectively solve problems. Because no one in the industry has long-term experience working with these methods, it is vital that the bioanalytical scientists that have made progress share their insights with others for the further advancement of techniques.
In the last five years, investigations of the gut microbiome, or microbiota, have skyrocketed largely due to advances in massively parallel sequencing technology and bioinformatic approaches such as the reconstruction of transcriptomes using de novo assembly in the absence of complete reference genomes. These metatranscriptomic studies of stool samples have identified and cataloged bacteria, fungi, and viruses that are common to healthy colonic microbial populations. For instance, over 35,000 bacterial species comprise the gut microbiota; although organisms belonging to the phyla Firmicutes and Bacteroidetes predominate1. Microbiome researchers have poured in a substantial body of work associating the dysbiosis of the microbiome with many pathologies including metabolic diseases, colorectal cancer, multiple sclerosis, cognitive developmental disorders, and autoimmune diseases2-10.
There has been a wave of companies rushing to leverage these connections to develop novel therapeutics. In fact, over 20 well-funded start-ups have recently surfaced with the mission to harness the power of the microbiome to treat and prevent disease. Of these microorganisms-orientated companies, Vendanta Biosciences (http://www.vedantabio.com/) is tackling solid tumors with bacteria, Seres Therapeutics (http://www.serestherapeutics.com/) is optimizing fecal transplants to treat recurrent Clostridium difficile infections, and Blue Turtle Bio (http://blueturtlebio.com/) is also utilizing bacteria from the gut microbiome but as a drug delivery platform. A Swedish company, Infant Bacterial Therapeutics (http://ibtherapeutics.com/) (IBT), which is taking on necrotizing enterocolitis (NEC) with Lactobacillus reuteri-based candidates, has been granted the Rare Pediatric Disease Designation by the FDA for its lead drug candidate to prevent NEC – a disease, which is fatal, especially for premature neonates.
The modulation of the gut microbiota composition of infants and children has been investigated as a therapeutic route for atopic dermatitis, bacterial gastroenteritis, inflammatory bowel disease, necrotizing enterocolitis, and allergic diseases11. Probiotic interventions encourage the growth of commensal bacteria and ward off the colonization of pathogenic organisms, thereby affording protection to the intestinal barrier function and reducing food allergies and atopic disease incident rates12. Infant formula containing low dose Bifidobacterium lactis supplementation is shown to provide similar early life outcomes to breastfeeding with regards to gastrointestinal infection rates and immune system and gut maturation13. However, benefits of probiotic usage are not limited to infants.
The Probiotics in Pregnancy Study conducted in Wellington and Auckland, New Zealand involved the administration of Lactobacillus rhamnosus four hundred pregnant women during pregnancy and breastfeeding. This study showed reduced rates of infant eczema and atopic sensitization at 12 months but also decreased rates of material gestational diabetes mellitus and the presence of bacterial vaginosis and vaginal carriage of Group B Streptococcus, and postpartum depression and anxiety14.
The potential of leveraging the data that can be gathered through deep microbiome profiling is strong and is increasingly becoming a promising strategic approach for drug discovery and development companies to treat infant, pediatric, and adult diseases.
- Jandhyala, S. M., Talukdar, R., Subramanyam, C., Vuyyuru, H., Sasikala, M., & Reddy, D. N. (2015). Role of the normal gut microbiota. World Journal of Gastroenterology: WJG, 21(29), 8787. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4528021/pdf/WJG-21-8787.pdf)
- Armougom, F., Henry, M., Vialettes, B., Raccah, D., & Raoult, D. (2009). Monitoring bacterial community of human gut microbiota reveals an increase in Lactobacillus in obese patients and Methanogens in anorexic patients. PloS one, 4(9), e7125. (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0007125)
- Brown, C. T., Davis-Richardson, A. G., Giongo, A., Gano, K. A., Crabb, D. B., Mukherjee, N., & Hyöty, H. (2011). Gut microbiome metagenomics analysis suggests a functional model for the development of autoimmunity for type 1 diabetes. PloS one, 6(10), e25792. (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0025792)
- Larsen, N., Vogensen, F. K., van den Berg, F. W., Nielsen, D. S., Andreasen, A. S., Pedersen, B. K., & Jakobsen, M. (2010). Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PloS one, 5(2), e9085. (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0009085)
- Weir, T. L., Manter, D. K., Sheflin, A. M., Barnett, B. A., Heuberger, A. L., & Ryan, E. P. (2013). Stool microbiome and metabolome differences between colorectal cancer patients and healthy adults. PloS one, 8(8), e70803. (http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0070803)
- Zhang, Y. J., Li, S., Gan, R. Y., Zhou, T., Xu, D. P., & Li, H. B. (2015). Impacts of gut bacteria on human health and diseases. International journal of molecular sciences, 16(4), 7493-7519. (http://www.mdpi.com/1422-0067/16/4/7493)
- Jangi, S., Gandhi, R., Cox, L. M., Li, N., Von Glehn, F., Yan, R., & Cook, S. (2016). Alterations of the human gut microbiome in multiple sclerosis. Nature Communications, 7. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4931233/)
- Cao, X., Lin, P., Jiang, P., & Li, C. (2013). Characteristics of the gastrointestinal microbiome in children with autism spectrum disorder: a systematic review. Shanghai Arch Psychiatry, 25(6), 342-53. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4054584/)
- Lyte, M. (2013). Microbial endocrinology in the microbiome-gut-brain axis: how bacterial production and utilization of neurochemicals influence behavior. PLoS Pathog, 9(11), e1003726. (http://journals.plos.org/plospathogens/article?id=10.1371/journal.ppat.1003726)
- Brusca, S. B., Abramson, S. B., & Scher, J. U. (2014). Microbiome and mucosal inflammation as extra-articular triggers for rheumatoid arthritis and autoimmunity. Current opinion in rheumatology, 26(1), 101. (https://www.ncbi.nlm.nih.gov/pubmed/24247114)
- Awasthi, S., Wilken, R., German, J. B., Mills, D. A., Lebrilla, C. B., Kim, K., & Maverakis, E. (2016). Dietary supplementation with Bifidobacterium longum subsp. infantis (B. infantis) in healthy breastfed infants: study protocol for a randomised controlled trial. Trials, 17(1), 340. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4957407/)
- Cao, S., Feehley, T. J., & Nagler, C. R. (2014). The role of commensal bacteria in the regulation of sensitization to food allergens. FEBS letters,588(22), 4258-4266. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4216641/)
- Baglatzi, L., Gavrili, S., Stamouli, K., Zachaki, S., Favre, L., Pecquet, S., & Costalos, C. (2016). Effect of infant formula containing a low dose of the probiotic Bifidobacterium lactis CNCM I-3446 on immune and gut functions in C-section delivered babies: a pilot study. Clinical medicine insights. Pediatrics, 10, 11. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792197/)
- Barthow, C., Wickens, K., Stanley, T., Mitchell, E. A., Maude, R., Abels, P., & Hood, F. (2016). The Probiotics in Pregnancy Study (PiP Study):rationale and design of a double-blind randomised controlled trial to improve maternal health during pregnancy and prevent infant eczema and allergy. BMC pregnancy and childbirth, 16(1), 133. (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4891898/)
Editor’s Note: The original article first appeared on the Ocean Ridge Biosciences website, which, after the acquisition of Ocean Ridge Biosciences, was modified for use on the Frontage website.
Tissue biopsies, which are currently the gold standard molecular diagnostic technique for oncologists, are not only painful to cancer patients but expose an already immune-comprised population to increased rates of nosocomial infections. Furthermore, it is not uncommon for the typical clinical pathology workflow to yield samples that are not suitable for routine analysis strategies, e.g., next-generation sequencing (NGS), due to severe degradation. Therefore, researchers at Abramson Cancer Center at the University of Pennsylvania (ACC) are among the many investigators pursuing reliable noninvasive tests to replace traditional tissue biopsies for the detection of reliable biomarker signatures and clinically relevant mutations. Recently, results from a study evaluating cell-free circulating tumor DNA (ctDNA) with blood samples derived from 102 advanced non-small-cell lung cancer patients (NSCLC) were published in Clinical Cancer Research in an article entitled, “Detection of Therapeutically Targetable Driver and Resistance Mutations in Lung Cancer Patients by Next Generation Sequencing of Cell-Free Circulating Tumor DNA” (Thompson et al., 2016). Of the patients in the study, 96% had stage IV disease, 81% had adenocarcinoma tumors, and 68% were women.
The study compared results from sequencing of focused gene panels that were independently developed for the analysis of tissue DNA and ctDNA. The tissue DNA samples were analyzed at the University of Pennsylvania (UP) using either the Illumina TruSeq Amplicon Cancer Panel (covers hotspots or exons of 47 genes) or a smaller UP Precision panel covering the 20 most commonly mutated genes, depending on the amount of DNA available. The ctDNA was analyzed using 68 or 70 gene versions of the Guardant360 panel; the sample processing and sequencing were performed by Guardant Health. The Illumina MiSeq instrument was used for the tissue DNA amplicon sequencing using an unstated sequencing depth, whereas the ctDNA amplicons were sequenced to a depth of 10,000X depth using an Illumina HiSeq 2500. Given the 4.2X higher yield of nucleotides from a single lane of the HiSeq 2500 versus an entire MiSeq run, the sequencing depth was likely lower for the tumor DNA samples. Sequencing was successfully completed using ctDNA template for all 102 patients. In contrast, sequencing of tissue DNA was successful in only 49% of patients due to poor quality of specimens, insufficient DNA yield, or because tissue biopsies were unobtainable.
Learn more about Frontage’s Cell-free DNA Methylation Sequencing Services.
Assuming faithful representation of the tumor genomes in the circulating DNA, because of the lower number of genes sequenced as well as the likely reduced coverage depth for the analysis of tissue DNA versus ctDNA, one would expect that the number of known mutations identified in the ctDNA would be higher than in the tissue DNA when comparing the same samples. This expectation was borne out by the focused comparison of 50 patients for which both tissue DNA and ctDNA were analyzed. Of these 50 common patients, 78% had at least one variant call from tissue DNA versus 84% for ctDNA. In keeping with the larger gene panel size used for the characterization of the ctDNA, an average of 2.8 variants per patient were detected in the ctDNA versus 1.5 in the tissue DNA. The overall concordance between the two sample types for variant detection was 60% when considering only polymorphisms that were covered by both the ctDNA and tissue DNA panels. When narrowing the scope to the therapeutically targetable EGFR mutations, the overall concordance in polymorphism detection between the matched tumor and circulating DNA samples improved to 79%.
The lower-than-desirable concordance rate (60%) should not be taken to indicate limitations in the reliability of the sequencing-based tests. The authors provide several plausible reasons for the reduced concordance between the ctDNA and tissue DNA assays. First, the time between the collection of the tissue sample and the blood sample for the study was up to 2 years, and major changes in the genetic makeup of the tumor could occur during this time. Another reason for reduced concordance could be the differing contribution of specific tumor lineages to tumor versus circulating DNA; in general tumor DNA may be diluted by non-tumor circulating DNA in the blood leading to a reduced frequency of specific mutations. Conversely, cell types with specific mutations may more aggressively shed DNA in to circulation and therefore be over-represented in the ctDNA relative to the tissue DNA. Overall, these results contribute to the promise that as sequencing technology continues to advance with the ability to profile lower amounts of DNA, with coverage of greater numbers of genes at greater depth, patients will be able to benefit greatly from non-invasive monitoring of the genetic make-up of ctDNA. Further improvements in databases of actionable mutations will allow for better clinical decision-making based on the review of ctDNA sequencing results, ultimately improving patient survival and quality of life.
Editor’s Note: The original article first appeared on the Ocean Ridge Biosciences website, authored by Sheena Knight, which, after the acquisition of Ocean Ridge Biosciences, was modified for use on the Frontage website.
Through liquid biopsy, studying cfDNA informs us on various considerations for drug treatment.
Authors: David Willoughby, Jessica Sinha
Precision medicine has seen many improvements in the current clinical approaches, especially as liquid biopsy methods are being increasingly studied. Liquid biopsy, which is an alternative sampling method for circulating macromolecules, has a particular emphasis on nucleic acids. Of particular interest is cell-free DNA (cfDNA), which is the extracellular DNA circulating in body fluid and can be derived from both normal and diseased cells and has the potential to be used as biomarkers. Patients that are likely to respond and those likely not to respond to a drug treatment during a late-stage clinical trial may be distinguished by an assay for specific biomarkers in a process termed patient stratification. With improvements seen in next-generation sequencing (NGS) tools, successful sequencing of cfDNA analysis is possible and is informative in various areas, from learning about mutations to monitoring targeted drug resistance.
For more on the current state and future of Precision Medicine and Companion Diagnostics, watch our panel discussion with Frontage expert, Dr. Kai Wang.
Liquid Biopsy: A Tool for Sampling Biomarkers
Liquid biopsy is a method of biomarker sampling that minimizes invasiveness, allows for routine sampling, and is associated with the analysis of circulating macromolecules with a particular emphasis on nucleic acids. Serum and plasma are the most common sample types, but urine, saliva, and other bodily fluids can also be used. Liquid biopsy is used for the determination of diagnosis or prognosis and to facilitate informed treatment decisions. Types of molecules that can be analyzed in liquid biopsy samples include lipids, carbohydrates, small biomolecules, proteins, circulating nucleic acids, or tumor cells. Particularly the circulating DNA and RNA assayed can be informative about a disease or drug therapies. However, for solid tumors or more organ-specific diseases, cfDNA or RNA is typically much more informative.
Methods for Cell-Free DNA Analysis
Frontage offers offer various methods to analyze cfDNA:
- Droplet digital PCR helps accurately measure copy number changes, single nucleotide polymorphisms (SNP), and small insertions/ deletions (indels) for one or a few loci.
- Real-time PCR is used to monitor SNPs, indels, conserved translocations, and methylation changes for one up to a few hundred specific sites (with qPCR panels). This method is highly sensitive with a wide dynamic range and can detect an extremely low % of variants in a population. However, this method is best suited for 1 or a few loci, though there are technologies that offer panels to run for multiple loci.
- Sequencing Techniques
- Whole genome sequencing is used mainly for the detection of major chromosomal rearrangements, deletions, and hypomethylation. This is not practiced for most applications for cfDNA given the non-uniform coverage of cfDNA.
- Amplicon sequencing is a low-cost and rapid procedure to target up to 200 genomic regions with commonly occurring polymorphisms that inform treatment decisions. Tumor mutational burden and microsatellite instability can also be determined.
- Hybridization-based capture sequencing allows sequencing of up to 100 million nucleotides from the human genome at high depth. This technique also enables the detection of potentially pathogenic SNP and indels, as well as some large deletions and gene fusion events.
- Methylation sequencing checks the methylation of cytosine residues in the DNA.
Major Challenges with cfDNA sequencing
- Low and variable yield of cfDNA: 10 nanograms of DNA, equivalent to 1520 diploid genomes, is the absolute minimum mass for reliably successful detection of somatic polymorphisms.
- Low percentage of circulating tumor DNA relative to total cfDNA: The percentage of tumor-derived DNA in circulation may be as low as 1%. One must sequence deep enough and use strong methods to prevent DNA loss during library preparation (point #4).
- Poor sample quality due to blood sample handling: This can cause contamination with DNA derived from the lysis of hematopoietic cells and inefficient removal of platelets.
- Loss of DNA during the preparation of libraries for sequencing: DNA loss during processing reduces the number of unique sequencing molecules represented in the sequencing library.
Frontage teams utilize commercially available amplicon- and hybridization-capture-based multigene sequencing panels from Illumina, Invitae, and other suppliers as well as custom sequencing panels focused on key target genes. A broad range of analysis capabilities such as droplet digital PCR (ddPCR) and qPCR are also available to Sponsors that choose Frontage.
If you’re enjoying this article, check out our Cell-Free DNA Methylation Sequencing services.
Analysis of cell-free DNA has been evaluated and studied in many fields from precision medicine, oncology, prenatal medicine, and transplant medicine, to cardiovascular diseases. Cell-free DNA analysis is an exciting space in the current drug development and diagnostic space, and cfDNA tests are being developed by many molecular diagnostics companies, the first ones being approved by the FDA in 2020: Guardant 360 CDx and FoundationOne Liquid CDx tests. Analyzing cell-free DNA enables better targeting of the populations that would respond best to the drug and has tremendous value in the early development of companion diagnostics. The rapid development of new molecular techniques propels the various uses and applications of cfDNA. Not only does it open doors to minimally invasive diagnostics, but it also delivers promising data that could improve clinical decision-making and support closer monitoring of drug responses.
Contract Research Organizations like Frontage assist sponsors with cfDNA analysis during clinical trials. Some common applications of cfDNA analysis are:
- Identification of classes of polymorphisms and mutated genes associated with experimental anti-tumor drug efficacy
- Identification of surrogate biomarkers of anti-tumor drug efficacy
- Companion diagnostics development
Frontage provides a wide array of genomic services for protein-, oligonucleotide-, gene-, and cell-based therapeutic discovery and development, and complete solutions for the analysis of RNA expression, DNA polymorphisms, methylation, microbial composition, and protein biomarkers. Our labs are optimized for ultra-low input requirements and challenging sample types, supporting mechanisms of action, lead optimization, biomarker discovery, and the development of companion diagnostics.
Adopted on May 24, 2022, the International Council for Harmonisation (ICH) has released a new guideline to standardize bioanalytical method validation (BMV) practices.
Until recently, different global regulatory agencies had varying requirements for validating bioanalytical methods and performing sample analysis. Now, the International Council for Harmonisation (ICH) has released a new guideline that aims to standardize bioanalytical method validation (BMV) practices.  The final step 5 ICH M10 guideline, adopted on May 24, 2022, has been ratified by various regulatory agencies including the U.S. Food and Drug Administration (FDA). For the EU’s European Medicines Agency (EMA), the ICH M10 will be coming into effect on January 21, 2023.
As already stated in global white papers [2,3] published after the public consultation period, ICH M10 does not seem to require significant changes compared to previous guidance/guidances. [4,5] Indeed, a white paper summarizing the 2019 Workshop on Recent Issues in Bioanalysis (WRIB) stated that the new guidance “has already been positively received by the global bioanalytical community and only a few major topics were identified which required further discussion.” 
Some recommendations may result in minor changes for certain types of validation tests and sample analysis procedures. Hence, it is important to work closely with a contract research organization (CRO) that is at the forefront of understanding and implementing these changes so you can continue to meet industry and regulatory standards without experiencing major disruptions in your projects.
For example: 
- In the 2018 FDA guidance , the lower limit of quantification (LLOQ), low-, mid-, and high-range quality control (QC) samples are recommended for accuracy and precision runs. ICH M10 specifies that for chromatography method validation, low-range QCs should fall within three times the LLOQ, mid-range QCs should be about 30 – 50% of the calibration curve range, and high-range QCs should be at least 75% of the upper limit of quantification (ULOQ). Ligand binding assays, on the other hand, require using the geometric mean of the calibration curve range for the mid-range QC sample.
- ICH M10 stipulates that QC samples must always bracket study samples, whereas the previous FDA guidance did not contain this requirement.
- Chromatography dilution QC samples need to be included during sample analysis. They must have a concentration that surpasses that of the diluted study samples. Alternatively, they need to have a higher concentration than the ULOQ. ICH M10 also states that if dilution QC samples for ligand binding assays have a concentration that exceeds the ULOQ, the concentration of the QC samples should be adjusted to represent the actual sample concentration range.
- The former FDA guidance required incurred sample reanalysis (ISR) for bioequivalence or pivotal pharmacokinetic or pharmacodynamic studies. ICH M10 also requires ISR for bioavailability studies, first clinical trials, pivotal early patient trials, and first trials in patients with impaired renal or hepatic function.
ICH M10 also includes slightly altered requirements for documentation and reporting. In some cases, additional data will need to be provided to regulatory agencies. For example, when performing comparative bioavailability and bioequivalence studies, the bioanalytical report needs to provide internal standard response plots from all runs. This also applies to failed runs. 
Some additional updates in M10 relative to the 2018 FDA guidance:
- The 2018 guidance specifies fresh QCs for P&A while M10 doesn’t but rather specifies fresh calibrators
- M10 has a section on analytes that are also endogenous but biomarkers are out of scope while the 2018 guidance has a small section on biomarkers
- M10 has overall acceptance criteria across runs for sample analysis data, consistent with the EMA guidance but not previously an FDA requirement
- The 2018 guidance was not specific about evaluating hemolysis or lipidemia. M10 has 1 lot of each in addition to the 6 selectivity lots.
- M10 has more detail on cross-validations
- M10 discusses dilution QCs in sample analysis including multiple dilutions in a run should have QCs at the highest and lowest dilutions
Other minor changes reflect current industry standards. For example, ICH M10 clarifies that the guideline was developed to support performing animal studies that follow the 3Rs — Reduce, Refine, and Replace. 
Frontage has expertise in carrying out studies according to the most recent bioanalytical method validation guidance/guidances across global markets. Contact us to learn more about our state-of-the-art bioanalytical solutions and discover how we can help you become compliant with ICH M10.
- Bioanalytical Method Validation and Study Sample Analysis: M10. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. Adopted May 24, 2022. Accessed October 14, 2022. https://database.ich.org/sites/default/files/M10_Guideline_Step4_2022_0524.pdf
- 019 White Paper On Recent Issues in Bioanalysis: FDA BMV Guidance, ICH M10 BMV Guideline and Regulatory Inputs: https://www.future-science.com/doi/pdf/10.4155/bio-2019-0270
- GCC Consolidated Feedback to ICH on the 2019 ICH M10 Bioanalytical Method Validation Draft Guideline: https://www.future-science.com/doi/pdf/10.4155/bio-2019-0207
- Bioanalytical Method Validation: Guidance for Industry. U.S. Food and Drug Administration. Published May 2018. Accessed October 14, 2022. https://www.fda.gov/files/drugs/published/Bioanalytical-Method-Validation-Guidance-for-Industry.pdf
- Guideline on Bioanalytical Method Validation. European Medicines Agency. Published July 21, 2011. Accessed October 14, 2022. https://www.ema.europa.eu/en/documents/scientific-guideline/guideline-bioanalytical-method-validation_en.pdf