Quantitative proteomics advances disease modeling and drug development

Disease models are essential in drug discovery and development, providing a means for studying disease mechanisms and evaluating drug efficacy and safety in a physiologically relevant context. Genomic and transcriptomic tools have long been used with these models to identify drug targets and observe gene expression changes during disease progression or therapeutic intervention. However, since RNA expression does not always correlate with protein levels, these approaches fail to fully elucidate the underlying protein networks and pathways driving disease pathology and therapeutic response.

Quantitative proteomics can address this gap. By directly measuring proteins – the primary drivers of phenotype and the targets of most drugs – proteomics can be used to better understand disease models and the subsequent effects of drug candidates. As a result, there has been growing interest in applying proteomics across the drug development continuum, from validating drug targets and evaluating therapeutic effects to uncovering the molecular mechanisms underlying diseases.

Diving deeper into disease mechanisms

Proteomics, although not a new tool, is yet to be widely adopted across the pharmaceutical industry. “It’s a relatively new concept for many biopharmaceutical and biotech companies,” Dr. Matt Westfall, Director of Research and Development at Inotiv, explains “While some may have in-house expertise in mass spectrometry or proteomics, this isn’t generally the norm.”

The complexity of proteomic techniques presents a significant hurdle for research and development teams eager to leverage its capabilities – which is where contract research organizations (CRO) like Inotiv come in. Inotiv supports R&D projects across North America and Europe by providing a wide range of research models and helping design and conduct in vitro and in vivo studies. Its proteomics offering– encompassing both targeted and global mass-spectrometry proteomics approaches – is designed to provide a highly collaborative framework to help drug developers better understand the biochemical pharmacology of their therapeutics.

“Through our targeted approach, we offer absolute quantification, providing molar concentrations of proteins of interest across any sample type, whether that be FFPE, plasma, serum, cerebrospinal fluid, cell pellets, or other tissue samples,” explains Westfall. “The global approach, on the other hand, focuses on pathway and network biology, providing a 30,000-foot view of the proteome to examine the overall biochemistry of a model and the therapeutic’s impact on it.”

This two-pronged approach offers R&D teams a powerful protein-level perspective on their target of interest while also quantifying disease phenotypes in models and assessing therapeutic effects. “With the informatics tools we have, we can not only confirm the biology or biochemistry of interest for a client, but also uncover novel biology,” explains Westfall. “Unexpected findings, such as significantly upregulated or downregulated proteins or pathways, can reveal new therapeutic opportunities or highlight potential off-target effects that clients may not have anticipated.”

Westfall also highlights the utility of proteomics in enhancing pathology studies. Here, techniques like immunohistochemistry (IHC) can provide spatial resolution or relative biomarker expression but cannot quantify those biomarkers. “Over the past six months, we’ve made strides in what we call quantitative pathology – where we provide quantitative levels of proteins which we can integrate with larger datasets such as RNA-seq or spatial transcriptomics,” he notes. “This additional layer of biochemical information provides a more comprehensive picture of therapeutic implications, providing valuable context for pathology studies.”

Using proteome data to select research models

In addition to targeted quantification, by analyzing the global networks and pathways involved in disease models, Inotiv’s proteomics platform can also provide insights into the overlapping biology of different diseases. For example, the company has observed that biochemistry and signaling pathways in ulcerative colitis IBD models can overlap with those in rheumatoid arthritis and immune components of oncology. Not only does this open new therapeutic opportunities for clients, but it also highlights alternative models for disease studies for a given drug candidate.

Westfall expands on the example of ulcerative colitis, for which Inotiv offers three rodent models, including a DSS sodium sulfate-induced model, an adoptive T-cell transfer model, and an IL-10 knockout model, which induces spontaneous ulcerative colitis. “In comparing these three models side by side, we’ve identified significant differences and similarities in the biochemical pathways involved,” he says. “Using pathway-level proteomic analyses, we can dissect these variations and examine the mechanisms behind the models to suggest those with the most relevant pathway for a given therapeutic.”

This in turn can help guide drug developers in selecting the most appropriate model for their therapeutic focus or biochemical target. “The DSS chemically induced model has been a long-standing gold standard, however, on a biochemical and physiological level, it doesn’t perfectly replicate how the disease naturally occurs,” says Westfall. “This type of analysis enables us to advise clients on whether they should use different models that more closely resemble human disease physiology – even if they differ from those traditionally used.”

Matching models to human disease

Westfall views the bridging of in vivo and in vitro models to human disease biology as the next frontier for drug pharmacology. “Evaluating various disease models to determine how well they reflect human biochemistry and phenotypes is the next step,” he explains. “Not only will selecting the correct model from the start save clients millions of dollars, but it will also significantly accelerate the transition from preclinical research to clinical trials.”

To achieve this, integrating multiple platforms like digital pathology, spatial transcriptomics, and proteomics across disease models and human disease studies will be key. And as these tools become increasingly sensitive – enabling researchers to do more with less–proteomics is poised to play an even greater role in driving breakthroughs in drug discovery and development.