Pharmacodynamic biomarkers are critically important in drug development to examine whether drugs are modulating their intended therapeutic targets or pathways, observed Jude O’Donnell, preclinical biomarker program team leader at Almac Diagnostics. She presented preliminary data demonstrating the potential of using peripheral blood in an ex vivo model system to discover potential pharmacodynamic biomarker candidates for an atypical antipsychotic therapeutic used in the treatment of schizophrenia.
“This study demonstrated the potential of using blood to quantitatively assess the exposure of a schizophrenia patient to an atypical antipsychotic regimen,” said Dr. O’Donnell. “This is of particular relevance for neurological disorders such as schizophrenia, where access to the primary tissue is difficult.” Biomarker research is becoming increasingly focused upon discovery within peripheral tissues such as blood, she added. “The ideal biomarker should be detected in a surrogate tissue thus avoiding the need to perform invasive tests such as biopsy.
“As effective determination of a biomarker can require taking multiple samples at multiple time points, the ease of peripheral blood collection offers obvious advantages over other tissues with respect to longitudinal sampling. Blood offers a wealth of material appropriate for biomarker research, ranging from circulating tumor cells, serum markers, and circulating nucleic acids.”
From a drug-development perspective, Dr. O’Donnell noted that the modern era of molecular therapeutics poses considerable challenges as the targeted nature of these new agents often reduces toxicities and renders assessment of adequate/optimal exposure difficult. “Often the biologically effective dose of novel agents is considerably less than the toxic dose.
“Escalating to just below toxicity can lead to drug failure in clinical trials as this increases the incidence of adverse events. Consequently, there is increasing emphasis on biomarker research at the preclinical and early clinical trial stages to provide a robust means of determining drug dose and effect.” Preclinical ex vivo modeling facilitates a time- and cost-effective means of assessing the feasibility of discovering a biomarker, pharmacodynamic or otherwise, in a peripheral tissue source such as blood, at an early stage in drug discovery and development.
Proteome complexity hampers protein biomarker identification, noted John C. Rogers, Ph.D., manager of mass spectrometry reagents at Thermo Fisher Scientific. “As we looked at what was problematic in biomarker research, we also wanted to differentiate with good science. Where are the pain points? Sample prep and data analysis are the leading culprits. We want to give scientists the right tools for proteomics success.”
Thermo Scientific cysTMT Mass Tag Reagents are thiol-reactive isobaric tags for labeling reduced cysteine residues to perform multiplex quantitative mass spectrometry analysis of protein expression.
When used in combination with the Immobilized Anti-TMT Antibody Resin, cysTMT tags reportedly allow specific labeling, enrichment, and quantitation of cysteine-containing peptides and proteins from complex mixtures of protein samples. Because of the relative proportion of cysteine residues in protein mixtures, it is a beneficial strategy to reduce sample complexity before MS analysis.
For TMT-labeled proteins and peptides, this can be accomplished using an immobilized anti-TMT monoclonal antibody that has high affinity (Kd<100 pM) for the reporter region of the TMT Reagent.
“Enrichment of labeled peptides by this method results in fewer peptides identified per protein but can significantly increase the number of unique proteins identified, making identification and quantitation of low abundance proteins easier,” said Dr. Rogers. “In addition to improving the dynamic range of sample analysis, cysTMT reagents can also be used to assess the oxidation or S-nitrosylation state of cysteine residues.
“When you look at the three things everyone is trying to optimize—sensitivity, scalability, and comprehensiveness—you have to understand that you can’t have all three. There is a trade-off. We choose to optimize for sensitivity and relevance during discovery, then sensitivity and scalability during assay development and validation. We are pushing for relevant proteins.”