The evolving role of investigative toxicology in the pharmaceutical industry

Abstract

For decades, preclinical toxicology was essentially a descriptive discipline in which treatment-related effects were carefully reported and used as a basis to calculate safety margins for drug candidates. In recent years, however, technological advances have increasingly enabled researchers to gain insights into toxicity mechanisms, supporting greater understanding of species relevance and translatability to humans, prediction of safety events, mitigation of side effects and development of safety biomarkers. Consequently, investigative (or mechanistic) toxicology has been gaining momentum and is now a key capability in the pharmaceutical industry. Here, we provide an overview of the current status of the field using case studies and discuss the potential impact of ongoing technological developments, based on a survey of investigative toxicologists from 14 European-based medium-sized to large pharmaceutical companies.

Antibacterial Photodynamic Therapy by Zn(II)‐Curcumin Complex: Synthesis, Characterization, DFT Calculation, Antibacterial Activity, and Molecular Docking

“…The cytotoxicity evaluation of any drug against human cells is a crucial step to ensure its safety for use in therapeutic applications [84–86] . Therefore, we assessed the cytotoxic effects of 1 – 3 on human normal cell lines, HEK‐293 (Human Embryonic Kidney), using MTT assay [87] .…”

“…The cytotoxicity evaluation of any drug against human cells is a crucial step to ensure its safety for use in therapeutic applications. [84][85][86] Therefore, we assessed the cytotoxic effects of 1-3 on human normal cell lines, HEK-293 (Human Embryonic Kidney), using MTT assay. [87] The cytotoxicity assay results demonstrated that 1-3 did not induce any adverse effect on HEK-293 cells under both, dark and light conditions (Figure 8).…”

Capture of the Mouse Organ Membrane Proteome Specificity in Peptidisc Libraries

“…For instance, several clinical diagnostics rely upon organ-specific protein biomarkers to monitor pathological states and disease progression, whereas the development of therapeutics critically depends on understanding organ specificity to prevent crossreactivity. 1,2 This knowledge is especially important at the cell surface since about two-thirds of the druggable proteome comprises integral membrane proteins. 3,4 Perturbation of the membrane proteome is also linked to neurodegenerative diseases, cancer, and disorders like hypertension and muscular dystrophy.…”

“…The precise knowledge of these organ-specific proteins, whose expression is significantly elevated in a given organ, holds great medical significance. For instance, several clinical diagnostics rely upon organ-specific protein biomarkers to monitor pathological states and disease progression, whereas the development of therapeutics critically depends on understanding organ specificity to prevent cross-reactivity. , This knowledge is especially important at the cell surface since about two-thirds of the druggable proteome comprises integral membrane proteins. , Perturbation of the membrane proteome is also linked to neurodegenerative diseases, cancer, and disorders like hypertension and muscular dystrophy. − Genomic and transcriptomic technologies have undeniably proven their worth by shedding light on organ protein expression patterns. − Still, to achieve a comprehensive understanding of organ biology at a molecular level, a direct estimate of its protein content is imperative. − …”

A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies

“…In silico methods are gaining popularity as a cost-effective and efficient alternative for drug toxicity assessment, with QuantitativeStructure–Activity Relationship (QSAR) being a commonly used approach. − However, QSAR has limitations, such as the requirement for large and diverse data sets, and the lack of standardization in experimental design and data reporting. , To overcome these limitations, researchers are exploring other in vitro/in vivo assay data such as omics data and high-content imaging. − ML/DL algorithms have also aided in silico methods for toxicity prediction, but their reliability and interpretability in real-world scenarios are limited. , Addressing these limitations, such as using diverse and high-quality data sets and robust validation protocols, is necessary for accurate and reliable prediction of drug toxicity. − …”