A D V E R T I S E R R E TA I N S S O L E R E S P O N S I B I L I T Y F O R C O N T E N T

A D V E R T I S E M E N T F E A T U R E

LPIXELLPIXEL: https://lpixel.net/enIMACEL: https://imacel.net/en

Harnessing AI to improve drug discoveryHow Daiichi Sankyo and LPIXEL unlocked the power of phenotypic screening.

Phenotypic screening enables researchers to iden-tify molecules that act on disease pathways in cells.Rather than starting with a hypothesis, research-ers run unbiased screens for drug candidates thatmodulate disease-relevant phenotypes, enablingthe identification of novel biology. Advances inimaging and data analysis are amplifying the powerof the approach. Recognizing that, Daiichi Sankyopartnered with LPIXEL, an expert in using machinelearning algorithms to analyse images.

The power of phenotypic screening was illustratedby a 2011 paper in Nature Reviews Drug Discoverythat analysed how new medicines were discoveredfrom 1999 to 20081.Over the period, target-basedscreening was a major focus but researchers alsoused approaches such as phenotypic screening andthe modification of natural substances. Despite thefocus on target-based screening, more first-in-classdrugs with new molecular mechanisms of actionwere discovered via phenotypic screening. Thefinding suggested phenotypic screening is betterat discovering novel candidates.

Use of phenotypic screening rose quickly in theyears following the publication of the paper butimplementation challenges limited its impact2. Inrecent years, researchers have identified the risein imaging throughput and of high-performancecomputing capabilities as the building blocks fora new approach to phenotypic screening3. DaiichiSankyo recognized the opportunity and partneredwith LPIXEL to seize it.

Developing a screening systemThe project grew out of Daiichi Sankyo’s desire toestablish a new method for compound screening.Daiichi Sankyo already had access to a range ofpowerful technologies for the discovery and evalu-ation of potential drug candidates, such as high-throughput screening and cell-based assays, butstill saw a need for a new, better way to determinewhether its compounds had potential. The needidentified by Daiichi Sankyo reflects an industry-wide desire to make better informed decisions earlyin R&D and thereby reduce attrition and bring morebreakthrough medicines to patients.

Daiichi Sankyo viewed LPIXEL as the companycapable of addressing its need for a new compoundscreening system. LPIXEL, which is based in Tokyo,is the developer of the IMACEL cell image machinelearning algorithm. IMACEL uses artificial intel-ligence (AI) to analyse images at a level of detailthat surpasses the human eye and at scales thatare beyond any workforce.

Global pharmaceutical companies and Japanesedrug developers, including Astellas and Takeda havepartnered with LPIXEL to use IMACEL in a range

of ways, for example to detect the chromosomalaberrations and breakages that indicate a moleculeis genotoxic or identify parts of a whole slide imagewith target molecule expression.

LPIXEL’s prior successes led Daiichi Sankyo toenter into a collaboration to understand whetherthe nuclear shape of cells could be used to assesstheir drug responsiveness. Work began in March2018. In the first stage of the research collaboration,LPIXEL and Daiichi Sankyo worked to define theexperimental conditions, imaging conditions, andevaluation indexes for how to capture the changesin the cells. The work was needed to find the rightconditions for machine learning. Once the condi-tions were in place, LPIXEL proposed a method ofnuclear staining to look at nuclear morphology.

The success of the first stage of the project enabledLPIXEL and Daiichi Sankyo to advance the collabora-tion in December 2018. In the second stage of theproject, the partners addressed the challenge ofhow to look at the morphology of the nucleus forscreening. Working with Daiichi Sankyo, LPIXEL builtan image analysis model by focusing on identifyingand annotating changes in individual cells. DaiichiSankyo helped to shape the model by providing inputon the actual mode of change caused by cell differ-entiation and by examining the imaging method. Inthe months up to August 2019, LPIXEL and DaiichiSankyo worked to further improve the accuracy ofthe annotation to create a more powerful algorithm.

Improving on the human eyeLPIXEL demonstrated the full power of the algo-rithm in the third phase of the project. Until thatpoint, the collaborators had assessed whether itwould be possible to train an algorithm to auto-mate image analysis that humans could performmanually. The large size of image databases andresultant impossibility of analysing them manuallymeans an AI that is as good as the human eye couldbe very useful, but LPIXEL aimed for an additionalbreakthrough in the third stage of the project.

From August 2019 to March 2020, LPIXELassessed whether the AI could capture and clas-sify the morphological changes in the nucleusthat cannot be seen by the human eye. The workinvolved the use of molecular biological markers

Fig. 1 | Phenotyping screening technology.

“ LPIXEL assessed whetherthe AI could capture and

classify the morphologicalchanges in the nucleus thatcannot be seen by thehuman eye

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A D V E R T I S E R R E TA I N S S O L E R E S P O N S I B I L I T Y F O R C O N T E N T

A D V E R T I S E M E N T F E A T U R E

and the creation and training of supervisory data.Through the use of molecular biological markers,LPIXEL and Daiichi Sankyo created an AI systemcapable of classifying morphological changes thatare undetectable by eyesight alone. The partnersused labels based on biological markers to classifychanges without the markers themselves.

Across the project, LPIXEL’s team, which consistsof AI engineers with backgrounds in biology, andresearchers from Daiichi Sankyo collaborated tobuild the experiments to collect the image data anddevelop the AI system. The result is an AI systemfor phenotypic screening.

LPIXEL and Daiichi Sankyo were able to create thesystem because they collectively have a rare mix ofassets and capabilities. A huge amount of imagingdata was needed to evaluate the drug reactivityof cells. The collaborators then needed to applytheir AI and biological expertise to create a systemcapable of determining each cell individually andquantifying the determination.

All cell images contain biological information butexactly what insights can be gleaned from the datadepends on the methods for imaging and analysis.Higher quality images can enhance performanceof the system. In other words, the better the datathat goes into the system, the better the insightsit generates. LPIXEL and Daiichi Sankyo deliveredan effective AI system for phenotypic screen-ing by optimizing each step in the process, fromthe volume and quality of images through to theAI-enabled analysis.

Uses of phenotypic screeningAI-powered phenotypic screening can analyseimages to capture both large morphologicalchanges occurring in cells and minor morphologicalchanges that are difficult to quantify visually. Thesystem thereby provides a more detailed, granularpicture of the behavior of cell populations thatcannot be captured by a simple average (Fig. 1).

In addition, the system can enable researchersto see morphology as a result of both the seed-ing signal response and various secondary signalresponses. As the system facilitates the evaluationof the final result of a drug, it empowers researchersto identify and select candidate molecules with themost favourable morphology.

Daiichi Sankyo is now using the AI-enabled sys-tem to evaluate the reactivity of cells to compoundsin its library, potentially leading to projects with thepotential to address major unmet needs of cancerpatients. However, the application of the AI tech-nology is far broader than the initial focus of thecollaboration between LPIXEL and Daiichi Sankyo.

The ability to quantify the morphology of each celland, in doing so, understand the properties of a cellpopulation and its responsiveness to different drugshas applications across multiple therapeutic areasand modalities. Applied to an anticancer drug, thesystem could evaluate slightly different responses ofeach cell to the molecule, leading to a more accurateimpression of the efficacy of the molecule.

Researchers can also use the technology to moreprecisely classify the morphology of cancer, forexample in the analysis of immature differentia-tion of blood cancer and subtypes of gastric can-cer. LPIXEL sees further opportunities to apply theAI-enabled image analysis system to the evaluationof cell preparation for regenerative medicine.

Opening access to the systemThe power of the system, which was validatedthrough the Daiichi Sankyo collaboration, andbreadth of its potential applications have ledLPIXEL to make the technology available to otherglobal pharma and biotech companies. The pheno-typic screening technology is a core componentof LPIXEL’s AI drug discovery support service,IMACEL Discovery.

Supported by IMACEL Discovery, global pharmaand biotech companies can use validated AI to rec-ognize, classify and quantify individual cells basedon complex characteristics of their morphology.The service will enable the accurate assessmentof drug responsiveness, as it did in the phenotypicscreening collaboration with Daiichi Sankyo.

IMACEL Discovery is part of a broader effort byLPIXEL to leverage the power of AI to improve R&D(Fig. 2). In the fall of 2021, LPIXEL plans to introducean AI-enabled safety testing product, IMACEL Tox.LPIXEL created the product by training an AI to learnevaluation criteria from experienced researchers.The result is a product that enables quantita-tive evaluation and automatic quantification. Byapplying AI to those areas, LPIXEL is facilitatingfast evaluations without imposing a heavy burdenon humans.

The screening and safety testing products arecomplemented by a third LPIXEL offering, IMACELClinical Study, that applies AI to medical imageanalysis to group patients suitable for clinical tri-als of candidate drugs. By grouping patients, LPIXELaims to help control the cost of clinical trials.

Across the three IMACEL products, LPIXEL isestablishing the capabilities to increase the suc-cess rate in R&D and accelerate the progress ofnew medicines by addressing causes of attritionand delays. In working toward that goal, LPIXEL hasaccumulated advanced technologies in coopera-tion with other companies, giving it the capabilitiesneeded to become a key player in the creation ofbreakthroughs in the pharmaceutical industry.

LPIXEL is now rolling out the AI drug discoverysupport service to other companies. By making theAI system for phenotypic screening and other drugdiscovery services widely available, LPIXEL aims tosupport the identification of new candidate com-pounds and improve the success rate of screening.In doing so, LPIXEL and its global pharma and bio-tech partners will usher in a new age of AI-enableddrug discovery that stands to deliver breakthroughtreatments that address major unmet needs.

1. Swinney, D. C. et al. Nat. Rev. Drug Discov. 10, 507–519 (2011).

2. Haasen, D. et al. Assay Drug Dev. Technol. 15, 239–246 (2017).

3. Scheeder, C. et al. Curr. Opin. Syst. Biol. 10, 43–52 (2018).

LPIXEL Science Biz. Dept.Tokyo, JapanTel: +81 3 6259 1713Email: imacel-support@lpixel.netCO

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Fig. 2 | IMACEL’s role in the drug discovery process.

“Supported by IMACELDiscovery, global pharma

and biotech companies canuse validated AI to recognize,classify and quantifyindividual cells based oncomplex characteristics oftheir morphology

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