AI-Powered Solutions for Organoid Research

Advancing Disease Modeling, Drug Discovery, and Personalized Medicine with Aganitha’s Innovative AI/ML Technologies

Context

Enhance organoid validation, characterization, and drug efficacy prediction

Organoids, three-dimensional cell cultures mimicking the structure and function of human tissues , are transforming drug discovery, disease modeling, and personalized medicine. Aganitha leverages artificial intelligence (AI) and machine learning (ML) to enhance different aspects of this research, such as model development, validation, drug efficacy testing, toxicity evaluation, cell differentiation analysis, disease modeling, and drug efficacy prediction.

Challenges in Organoid Research

Organoid research faces several key challenges that hinder progress and efficiency.

Data Complexity

Organoid studies generate multi-dimensional datasets spanning images, functional data, genomics, transcriptomics and proteomics. Integrating and analyzing these diverse data types from various experimental techniques poses significant challenges.

Variability

Ensuring reproducibility and standardization across organoid cultures and experiments.

Time-Intensive Analysis

Novel techniques and softwares available to interpret data are still slow, requiring significant researcher time and attention to minute details.

Predictive Modeling

Difficulty in building  capabilities to predict complex outcomes like drug efficacy and toxicity.

Aganitha's AI/ML Solutions for Organoid Research

Aganitha provides a suite of AI/ML-driven solutions designed to address the specific needs of organoid researchers:

Solution area

Disease Modeling & Drug Discovery:

Predictive Modeling of Disease Progression:

  • Develops predictive models for disease progression using organoid model data
  • Example: Using bright field images of iPSC-based midbrain organoid models of Parkinson’s disease to build predictive models
    • AI model trained on imaging data and features like neurite length
    • Successfully predicts disease progression
    • Aids in understanding disease mechanisms and identifying potential therapeutic targets

Predicting Drug Efficacy and Resistance:

  • Models trained on organoid imaging, transcriptomic, and proteomic data of treated and untreated patient organoids
    • Predicts drug efficacy and identifies potential resistance mechanisms
    • Focuses on pathway networks relevant to the drug target
    • Provides insights into treatment strategy effectiveness

Developing Toxicity Evaluation models on Organoid data:

  • Treating organoid co-culture models to obtain complex cell interactions data, models developed can

    • Use brightfield imaging of organoids to assess toxicity
    • Allow for more accurate assessment of potential adverse effects such as Predict bispecific antibody derived intestinal toxicity 
    • Evaluate potential toxicity of drug candidates
    .
Solution area

AI as research collaborator (ARC™)

 An Omics Bot to Democratize Data Analysis. Learn more about ARC™.

ARC™ is an AI-powered tool designed to simplify data analysis.

The conversational interface lets researchers interact with their data, generate visualizations, and gain insights without extensive programming knowledge.

The Omics Bot supports public and private data sources, making it a versatile tool for organoid research.

Outcomes

Benefits of Aganitha's AI/ML Solutions:

Validating Your Organoid Models with Precision

Our machine learning models help you validate your in vitro organoids for differentiation.. For instance, you can identify cell types, and reveal gene expression patterns in brain organoids, ensuring your models accurately represent human organs.

Effortlessly Track and Characterize Your Organoids:

Our deep learning algorithms automatically analyze images and videos of your organoids, providing you with real-time information about their development and response to environmental changes. You can detect changes in organoid morphology, size, number, and functioning, enabling more efficient analysis of large datasets for high-throughput screening.

Uncover Subtle Morphological Changes:

Our advanced deep learning tools help you capture subtle changes in organoid morphology that traditional methods might miss. For example, you can detect opacity and budding of colorectal organoids and predict retinal differentiation based on brightfield images. For high-throughput drug screening, our deep learning autoencoder-decoders capture the underlying phenotypic structures of organoids.

Streamlining Your Drug Discovery Process

Our AI/ML tools empower you to assess drug toxicities, predict drug responses, and identify new therapeutic targets based on omics data. You can predict tumor organoid drug-resistance, neurotoxicity in brain organoids and identify biomarkers for drug response. 

Discover our offerings across the biopharma value chain

Our Solutions

Customizable platforms and solutions across the drug discovery value chain from target discovery to therapeutic development
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Our Services

Offering services in computational sciences and technology to complement biopharma R&D

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Case Studies

Helping clients accelerate drug discovery and development by building computational biology, computational chemistry, technology, and cloud solutions
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