qPCR Analysis: A Comprehensive Guide for Drug Development

qPCR Analysis: A Comprehensive Guide for Drug Development
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The introduction of qPCR-based techniques complemented with human genome projects and microarray-based experiments have resulted in numerous DNA and RNA-based assays in drug discovery and development studies. However, quantitative assessments of DNA and RNA levels should be adequately validated to ensure that the data is of high quality and provides meaningful information. Hence, when it comes to qPCR assays, accurate bioanalysis is critical for drug development studies such as assessments of PK samples in clinical trials. The current article is a comprehensive guide on qPCR assays in drug development. 

qPCR analysis

qPCR assays provide accurate copy number determination and gene expression analysis. The absolute and relative quantification approaches help researchers to determine mRNA expression of pharmaceutical drug targets, predictive biomarkers, and PD markers. Moreover, qPCR assays support drug discovery projects based on animal and cellular models as well as study samples. qPCR assays have numerous applications in drug development studies, including:

Quantification of target gene expression to identify suitable tumor cell lines

Screen biomarkers to predict drug responses in animal or cellular models

Evaluate pharmacodynamic properties that may influence gene expressions

Genotyping to verify stratification markers

Multiple PCR-based tools such as RT-PCR, real-time PCR, and ddPCR assays

Researchers often establish sample preparation protocols for tissues and cells. Besides, other sample options include whole blood, 3D cell culture, cell-free samples, fixed formalin paraffin-embedded samples, and circulating DNA. 

Furthermore, bioanalytical laboratories have dedicated sample preparation and qPCR assessment protocols supported by state-of-the-art equipment. Besides, all standard procedures and protocols are verified to deliver high standards and reduce cross-contamination risks. 

Today, biomarkers are integral to drug discovery and development studies. They are vital for transitioning potential drug compounds from early discovery studies into later stages of clinical drug development. Earlier technologies such as HPLC, immunoassays, and enzymatic assays were employed to determine biomarkers such as metabolites, proteins, and peptides. However, qPCR assays are employed rapidly to study potential new biomarkers. 

Drug developers rely on biomarkers during early discovery studies to establish proof of concept studies and identify appropriate dose ranges for later phase 2 and 3 clinical trials. By combining PK/PD data, drug developers can evaluate the effects and relationship between concentration and observed therapeutic outcomes. Today, there is an increasing need to complement existing microarray-based techniques with more quantitatively accurate qPCR assessments. 

Today, RT-PCR has several different detection strategies for detecting biomarkers and other metabolites. Besides, there are different quantitative approaches for real-time PCR assessments, including the relative quantitative approach, comparative cycle threshold approach, relative standard approach, internal standard approach, and external standard approach. Each of these strategies has unique advantages. However, irrespective of the approach selected, analytical validation of these methods remains crucial for generating accurate, reliable, and reproducible results. The parameters of analytical validation of qPCR assays include calibration curve, normalization or reference gene, lower limit of quantitation, accuracy, precision, the upper limit of quantitation, specificity, and stability. 


The analytical validation of qPCR assays has been an iterative approach over many years. The use of microarray-based techniques will continue to drive the need for qPCR assays with high standards and accuracy. Although analytical validation of qPCR assays considers several aspects of the diagnostic assays, researchers must also focus on addressing several drug-specific issues.