A Comprehensive Guide to Optimizing Your Small Molecule Drug Development Strategy 

Given that small molecules make up more than 50% of the drug development pipeline, adopting a proactive non-clinical to clinical strategy is crucial for accelerating your program.

A holistic bioanalysis strategy that incorporates spatial analysis allows researchers to produce quality data, identify drug targets, evaluate efficacy, and optimize delivery. Join industry experts Troy Voelker and Corinne Ramos as they cover a step-by-step approach to a rugged method, including:

  • Strategies moving from pre-clinical to clinical
  • Spatial bioanalysis and moving into GLP
  • Molecule stability and reproducibility
  • Handling matrices and compounds
  • Different instrumentation and uses
  • Cold storage check list
  • Ongoing use of ICH M-10

View our on-demand webinar to learn how to implement a comprehensive strategy that expedites the development of your small molecule.

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Oligonucleotides: A Bioanalytical CRO’s Perspective on Current and Future State

Since the first oligonucleotide (OGNT) drug was approved by the FDA for clinical use in 1998, the industry has made significant progress in the advancement of techniques, technology, and development methods. However, due to the complexity and sensitivity of OGNTs, there are still challenges to navigate and overcome within drug development and bioanalytical analysis.

Join oligonucleotide bioanalysis SME Troy Voelker, Director of Laboratory Operations at Aliri, as he discusses:

  • Current and future state of the bioanalysis of OGNTs
  • Common approaches to bioanalysis of OGNTs
  • Evaluating and selecting the optimal and strategic approach

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Investigation of immune-checkpoints for personalized therapy selection

 

Monoclonal antibody-based therapy targeting PD1 blockage has brought a transformative shift in the immunotherapeutic strategy against solid tumors. However, the limited effectiveness of this treatment stems from the absence of precise methodologies, such as immunohistochemistry, for identifying patients who could potentially respond well to immune checkpoint inhibitor therapy.  
 
Because a single biomarker is not accurate enough to predict the interaction of the drug at the site of action, we developed a strategy to investigate the complexity of the tumor microenvironment, while also guiding patients towards combination therapy. Using multiplexed high throughput analysis, we have been able to investigate pathways of immune modulation at the molecular level to drug response and resistance. 
 
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Sequential pathogenic events in Type I Diabetes

 

Type 1 diabetes (T1D) arises due to the autoimmune degradation of insulin producing β cells. In order to cure or halt this disease, understanding how cell types, cell states, and cell-cell interactions evolve during T1D development is essential. 
 
Utilizing our proprietary imaging analysis tools, we were able to segment cells to identify cell populations for spatial analysis and further classification in diabetes with precision. Our deep data analysis workflows and advanced imagine techniques introduce new opportunities to investigate the pathology of T1D within the pancreas. 
 
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Spatially-resolved tumor gene expression analysis

 

As cancer advances, tumor cells come into contact with new cell types within the microenvironment, but it is still unclear how the tumor cells adapt to new environments. Spatial transcriptomics is a powerful approach to uncover mechanisms that allow tumors to invade the microenvironment and help discover biomarkers for potential therapeutic targets.

In this application note, we outline how we fine-tuned and merged spatial transcriptomics with laser microdissection (LMD) to identify distinct patterns in genes in tumor cells throughout the stages of cancer progression. 

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Intra-tumoral metabolic plasticity

Just as genetic diversity varies, the metabolic characteristics of cancer exhibit significant heterogeneity due to varied signals within the tumor microenvironment. Hence, addressing metabolic adaptability is crucial when developing cancer immunotherapy strategies.

Utilizing our proprietary Mass Spectrometry Imaging and GeoMx platforms, we were able to evaluate the interactions between metabolic pathways and functions of immune cells to assist in the development of new cancer metabolism drugs. A combined workflow of Quantitative Mass Spectrometry Imaging (QMSI) and GeoMx allows for a better understanding of both direct and indirect modulation of anti-tumor immunity through a better understanding of the tumor immune cell interface.

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Immuno-oncology, T cell metabolic adaptation

 

The tumor micro-environment is marked by a consistent decline in oxygen levels and nutrients carried by the bloodstream, which significantly impacts the metabolism of specific groups of cells. To overcome tumor-prolonged nutrient deprivation, immune cells populating malignant lesions need to activate alternative pathways.

Tailoring immune responses by manipulating cellular metabolic pathways provide new options for cancer immunotherapy. Our team conducted a Quantitative Mass Spectrometry Imaging (QMSI) data exploration study on stage II colorectal cancer tissues to reveal the role of metabolic signatures in anti-tumor immunity. Based on metabolic clusters found within the TME, predictive metabolic signatures can be identified that infiltrate levels of immune cells, which can be a powerful tool for predicting immunotherapy responses.

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Immunologic Alterations in the Lung Cancer Environment

 

Immunotherapy has transformed the landscape of lung cancer treatments but despite promising outcomes, only a small fraction of patients experience substantial benefits from immune checkpoint inhibitor (ICI) therapy.

Utilizing our proprietary platform, our team has been able to identify spatial biomarkers in the context of lung cancer disease that help the further classification of patients that could benefit from ICI therapy. By examining the effect of spatial immune biomarkers in the tumor’s microenvironment, we observed the tumor’s response to ICI, and evaluated gene expression profiling data to gain insights about patients’ response.

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Delineation of cell subpopulations and cell-cell interactions

 

Understanding the complex interactions in the tumor microenvironment (TME) plays a pivotal role in the onset of cancer, the advancement of tumors, and the determination of the reaction against anti-cancer treatments.  
 
Traditional studies fail to recognize the potential of studying the spatial arrangement of the TME and are therefore unable to completely uncover its intricacies. To overcome these limitations, we use quantitative image analysis based on multiplex immunohistochemistry to extract data from the TME. 
 
Discover how our proprietary imaging platform helps identify drug targets in spatial environments and provides insights into treatment response for non-small cell lung cancer. 

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Spatial quantification of biomarkers within target tissues with image analysis platform

 

Exploring tissue-based biomarkers is a crucial component of pre-clinical/clinical drug development, as it can help identify new therapeutic targets, evaluate surrogate markers of drug effectiveness, and predict potential benefits of a candidate compound.

Using image analysis allows us to evaluate tissue biomarkers in greater detail and study cellular interactions in complex biological processes. Our digital pathology data analysis workflow allows the automation of cell segmentation and their classification to resolve the molecular architecture of the tissue.

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