Continuous manufacturing (CM) in the pharmaceutical sector integrates the various discrete stages of traditional batch production into a continuous process, significantly decreasing drug product manufacturing time. In CM, where all process units are directly linked, it is crucial to continuously monitor the current process state and maintain consistent product quality throughout...
The landscape of the pharmaceutical industry is evolving. From what was (and still is) a science-centered discipline, more awareness exists nowadays of the opportunities arising from exploring data-driven methodologies to conduct various key activities. In this regard, Chemometrics has been an old-standing ally of the pharmaceutical industry, allowing for real-time assessment of raw materials,...
Monitoring disease prevalence over time is critical for timely public health response and evidence-based decision-making. In many cases, prevalence estimates are obtained from a sequence of independent studies with varying sample sizes, as commonly encountered in systematic reviews and meta-analyses. Traditional control charts such as the EWMA and CUSUM have been widely used in industrial...
In pharmaceutical statistics, traditional outlier detection often focuses on univariate methods. However, a multivariate approach is essential for analysing complex datasets representing critical quality attributes, such as assay, dissolution, and disintegration time.
The Shiny-for-Python application described here employs advanced machine learning techniques, specifically Principal...
An important parameter in pharmacological research is the half-maximal inhibitory concentration (IC50/EC50), which quantifies the potency of a drug by measuring the concentration required to inhibit a biological process by 50%. The 4-parameter logistic (4PL) model is widely employed for estimating IC50/EC50 values, as it provides a flexible sigmoidal fit. Meta-analysis on the other hand, has...
Single-cell RNA sequencing (scRNA-seq) enables detailed exploration of cellular heterogeneity,Yet its high dimensionality requires efficient feature selection for robust downstream analysis.This study evaluates five feature selection methods—Triku, Scanpy, Seurat, Variance Threshold, and Pearson Residual—on a multi-cancer scRNA-seq dataset comprising 801 cells from breast, colon, kidney, lung,...