Biomarkers increase the success rates of drug development programs, accelerating the path to market and the availability of new therapeutics. Modern translational strategies and precision medicine are rooted in the use of biomarkers – it is estimated that three quarters of the drugs in development in major pharma companies utilize biomarkers in clinical studies. With diverse applications from basic research to post-market safety assessments, drug discovery and clinical teams are leveraging biomarkers to improve target selection, predict drug safety and select trial participants.
Biomarker development is an iterative, multi-step process beginning with discovery, and requiring different levels of validation through analytical validation, clinical qualification and clinical utility. AI can be used to catalyze this process and enable early drug discovery teams to transition discovery projects into clinical programs with confidence.
Identify novel and validated biomarkers
- Easily discover biomarkers - guide your search based on novel biomarkers for more exploratory projects, or focus on biomarkers that are clinically qualified with supporting genetic evidence or demonstrated clinical significance.
Explore a range of applications from preclinical to clinical
- Use Causaly to identify different classes of biomarkers, ranging from biomarkers of disease progression, diagnostic, efficacy and safety biomarkers for early discovery and pre-clinical use cases, through to clinical biomarkers that are more indicative of survival, treatment response and drug resistance.
Prioritize and qualify biomarkers
- Use a range of parameters in Causaly to easily prioritize biomarkers, including the strength of disease linkage, class of biomarker, where it is expressed, availability of preclinical or other experimental data and more. Tailor your process to match your workflow.
- De-risk the selection of biomarkers by considering the availability of assays and measurement methods to detect biomarkers in humans.
Identify novel and validated biomarkers
- Easily discover biomarkers - guide your search based on novel biomarkers for more exploratory projects, or focus on biomarkers that are clinically qualified with supporting genetic evidence or demonstrated clinical significance.
Expedite Biomarker Discovery and Characterization
Evaluate the biological function, expression patterns, disease association, MoA, and relevance to clinical outcomes of biomarkers without the risk of overlooking potential candidates.
Rapidly Validate Biomarkers
Discern true biomarker candidates by effectively filtering out false positives and running comparative analyses in Causaly on experimental data.
Optimize Experimental Design
Determine experimental protocols and biomarker feasibility for use in early clinical trials.
Biomarkers Resources
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Comparison of Safety Biomarkers for Chemotherapeutics
The identification and utilization of safety biomarkers plays a key role in mitigating toxicity risks and reducing costs in drug development, thereby accelerating the delivery of safe and effective drugs to patients. AI can streamline the identification of relevant biomarkers from the ever-growing biomedical literature, offering insights into drug resistance and toxicity.
Challenging the Status Quo: A Biomarker Use Case
Traditional keyword searching is highly inefficient, subject to bias and is not always comprehensive, providing limited potential for knowledge discovery and hypothesis generation. This selective approach introduces a bias towards familiar areas of expertise, which can lead to missed opportunities for novel insights and innovations. This is where AI comes in.
Biomarkers of Treatment Response in Liver Cancer
Biomarkers serve as objective measures of treatment response to guide patients towards the most appropriate therapies. Yet, in the era of big data, pinpointing promising biomarkers remains a challenging endeavor. AI is revolutionizing translational medicine by improving the efficiency and accuracy of biomarker identification. Here, we used Causaly to identify and prioritize biomarkers of sorafenib […]
Expedite Biomarker Discovery with Human-Centric AI
Biomarkers are pivotal throughout drug development, from discovery to market, playing key roles in unravelling drug mechanisms, providing prognostic insights and assessing treatment efficacy. Despite the clinical promise, biomarker development is challenging. There are substantial obstacles, from disease heterogeneity and rigorous validation requirements to the inability to extract meaningful biomarker insights from extensive biological data.
Biomarker Identification and Prioritization: GERD
Biomarker-based strategies have shown to improve drug program success. However, biomarker discovery is marked by the intricate challenge of extracting relevant molecular signatures from extensive biological data. In this use case, we demonstrate how Causaly can be used to understand the biomarker landscape in GERD, a common digestive disorder characterized by chronic acid reflux.
Biomarkers of Imatinib Resistance in Chronic Myeloid Leukemia
With oncology pivotal trials averaging $32 million and a 95% drug failure rate, there is a growing interest in leveraging biomarkers for treatment optimization. Notably, drug resistance biomarkers are key in identifying treatment sensitivity, adjusting treatment protocols, and improving survival rates.
Identifying Prognostic Biomarkers of Pancreatic Cancer
Pancreatic cancer is one of the most aggressive and fatal cancers worldwide. Treatment efficacy is a major concern, and there is a pressing need for reliable prognostic biomarkers to improve patient outcomes. Here, we used Causaly to uncover potential biomarkers for this disease.
Identifying Urine Biomarkers of Chronic Kidney Disease
Monitoring disease progression is crucial for timely interventions in CKD. Urine biomarkers, such as RBP4, offer potential in CKD detection and disease tracking. Using Causaly, Retinol-Binding Protein 4 (RBP4) was identified as a potential urine biomarker of CKD progression with untapped potential.