Exploring New Assays to Identify Pharmaceutical Drug Candidates Using Innovative Software


biovia-experimentLife sciences companies are branching out beyond standard cell culture techniques to implement new drug candidate identification assays that utilize more accurate disease models. Image Credit: Flickr user Alberta Advanced Education

It is no exaggeration to say that traditional cell- and tissue-based disease models are at the heart of the drug discovery process, and that they have helped scientists develop revolutionary  therapeutics for a wide range of disease treatments. However, as a recent review article in the journal Nature notes, they have also proven insufficient for identifying cures to many diseases, including aggressive cancers and neurodegenerative diseases like Alzheimer’s and Parkinson’s.

Specifically in cancer research, standard cell line screens and in vivo xenograft models have led to the identification of drugs that work in the lab, but have not worked in the clinic because they fail to fully account for physiological nuances like tumor heterogeneity, drug penetration through outer tissues and the interactions between cancer cells, healthy cells and the stroma. For neurodegenerative diseases, it has been difficult to even identify possible drug targets using today’s cell- and tissue-based candidate screening methods.1 In attempts to find treatments for other ailments, preclinical models have failed to highlight the cardiotoxicity and/or hepatotoxicity of candidate drugs, so life sciences companies have invested millions in development, only to be forced to abandon a candidate in clinical trials because of dangerous side effects.

In response to these concerns, scientists are embracing more recently developed technologies that have greater disease relevance and can be used for assays that better represent the drug’s performance in the physiological environment. Modern software can streamline the process of replacing old disease models with more representative alternatives, such as:

  • Primary and patient-derived cell models, which better represent a disease situation than immortalized cell lines that lose physiological characteristics over time due to genetic drift
  • Induced pluripotent stem cell models, which allow researchers to focus on whichever cell type they are interested in
  • 3D cell culture models, which can better represent the relationship of a cell with other cells and its microenvironment than a 2D plate-based model
  • Organ-on-a-chip technology and microfluidic systems, which facilitate long-term in vitro cell growth without the risk of drift and make it possible to model dynamic factors like blood flow

At the same time, better microscopy, image analysis and bioinformatics tools are making it possible to interpret data more accurately, and genome editing tools can help scientists develop an even wider range of disease-specific models in the future.

Bringing New Candidate Identification Technologies to the Lab

While the advantages of some of these new drug candidate identification techniques over traditional methods are clear, it can be complicated to implement them in the lab. In order to ensure accurate data, it is essential for all procedural steps and equipment usage to be standardized, so that methods stay the same even when conducted by different researchers. Modern software makes this easier for research groups that adopt new candidate identification assays, and it also enables the automation of research tasks wherever possible, which can further reduce the risk of human error. That way, even when conducting a novel assay that requires unfamiliar technology, research groups can be confident of the veracity of their results.

It is also important for researchers and groups within a life sciences company to be able to share information about a new target selection experiment or lead identification assay. Within the group, researchers who are conducting the same assays can compare data in order to ensure that their processes are consistent. Moreover, as researchers identify trouble spots and optimize particular procedures, they may share their strategies across the company, contributing to a reservoir of institutional knowledge about a new drug candidate identification technology.

Streamlining the Candidate Identification Process

While the adoption of an unfamiliar technology can sometimes contribute to a slowdown in research, modern laboratory unification software can help integrate the procedure into the lab’s existing drug discovery, development and testing process in a way that keeps the research moving forward without compromising operational quality. As a result, the company will be able to bring novel therapeutics to market as quickly a possible.

Improving research efficiency is also essential because, according to the authors of the Nature review, some of the new candidate identification technologies are more expensive than traditional cell culture techniques. Using modern software to standardize processes can lead to less experimental redundancy and material waste, reducing the overall duration of the R&D process and minimizing overhead costs. Since modern software will allow the company a greater return on its research investments, employing cutting edge technologies that are slightly more expensive becomes more economically feasible.

BIOVIA ONE Lab is an innovative laboratory unification software package that offers research process improvements for life science companies that choose to replace traditional drug candidate identification assays with revolutionary alternatives. By offering collaboration and method standardization capabilities, they cut down on development time while also guaranteeing operational excellence. Contact us today to learn more about this and our other software solutions.

  1. “Screening out irrelevant cell-based models of disease,” September 12, 2016, http://www.nature.com/nrd/journal/v15/n11/full/nrd.2016.175.html