A Case Study in

Drug Discovery


Modelis is a small biotechnology firm specializing in drug discovery for rare genetic diseases. Their innovative approach to drug discovery leverages an in vivo high-throughput screening platform in model organisms, combined with computational data mining.

Precision Analytics was enlisted to improve the efficiency of data analysis and reporting, which were becoming more demanding as screening throughput increased.

We made numerous statistical recommendations and implemented modifications to their compound activity scoring approach, aiming to improve hit discrimination within the inherent variability of an in vivo environment. Reproducible analysis and reporting practices facilitated faster results turnaround and shorter time to hit confirmation.

The Story of Modelis

Modelis leverages advanced data generation methods to reduce the time and resources it takes to uncover data-driven mechanistic insights into rare disorders, thereby enabling faster drug discovery for these diseases. Their innovative approach identifies new or proprietary molecules from their partners for a wide range of human disorders.

The opportunity of accelerated drug discovery

Modelis was looking to increase throughput across their screening programs through more efficient data analysis and reporting.

Their existing workflow relied on spreadsheet software and statistical software that required manual data manipulation. These steps had become labor intensive due to the volume and complexity of screening data.

Faster data analysis represented an opportunity to accelerate drug discovery using their platform.

Why Modelis chose Precision Analytics

Modelis was seeking support from an external data science team that could provide both statistical consultation and hands-on, technology-enabled analysis support. Precision Analytics’ expertise in bioinformatics, statistics, and life sciences were critical.

How Precision Analytics responded

We entered an exploratory phase of work, during which we reviewed sample datasets, consulted published literature, and discussed the client’s needs. These early meetings helped us understand the complexity of their data, and helped us design a customized solution that met their needs.

We recommended modifications to the normalization and scoring algorithms that would be practical to implement, but that have been proven to better account for plate-to-plate variability and improve hit sensitivity. After discussion and an implementation phase, we began analyzing new HTS experiments according to the agreed upon protocol.

Analyses were conducted reproducibly in statistical software to ensure consistent and accurate reports.

The Results

We increased the speed of data analysis 2-3 fold and reduced our client’s in-house data analysis workload. This increased their capacity for screening and reduced the potential time to hit confirmation, accelerating the pace of their R&D efforts and making them more profitable for their CRO arm.

Furthermore, our publication-quality tables and figures are considered the “gold standard” by our client, facilitating effective communication with stakeholders and better scientific decision making.


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