Impact of Data Harmonization on Machine Learning Models in Drug Development

Inconsistent data formats and fragmented datasets can severely limit the effectiveness of machine learning models. These challenges necessitate data harmonization to produce effectual results. For example, the COVID-19 pandemic revealed the need for harmonized clinical and genomic data to accelerate drug discovery endeavors. AstraZeneca's AI-driven models benefited from harmonized datasets to identify new potential drugs for COVID-19. Similarly, harmonization was pivotal in Pfizer's machine learning models to improve clinical trial outcomes. Without harmonized data, these ML-driven breakthroughs in drug development would face roadblocks, and impair innovation and patient outcomes. This blog explores the role and impact of data harmonization on the efficiency and accuracy of ML models in drug development.

ML Initiatives in Drug Discovery: Significance of Data Harmonization

Data harmonization involves standardization and integration of diverse data sources in a unified format. The key steps in data harmonization include data cleaning, standardizing formats, normalizing data to reduce biases, and ensuring consistent metadata tagging. These steps not only make datasets more comparable but also enhance the performance of ML models by ensuring that they are trained on clean, reliable, and well-structured data. The role of data harmonization becomes indispensable in drug development owing to its need for precision. Its significance is highlighted below:

Integration of Diverse Biological Data: Biological data is often generated using a wide array of methods, instruments, and standards in the process of drug development. Data harmonization enables the seamless integration of these diverse data types and generates comprehensive datasets that can be utilized effectively by ML models.
Enhanced Predictive Accuracy: Harmonized multi-omics data significantly boosts the predictive accuracy of ML models by uncovering patterns which isolated datasets may fail to detect. This leads to accurate drug target identification, biomarker discovery, and patient stratification, all of which are crucial for successful drug development.
Consistent High Quality Data: High-quality harmonized data ensures that ML models are trained with consistent and standardized inputs. This consistency is critical to minimize the risk of inaccurate predictions, which can delay drug discovery or lead to failed clinical trials.
Advanced AI Applications: Advanced AI methods, such as deep learning and generative models, which are commonly used in drug development, require large volumes of high-quality harmonized data. Without harmonization, inconsistencies across datasets can degrade model performance, limiting the potential for accurate and actionable insights.

Challenges: Harmonizing Biological Data for ML Models

Harmonizing biological data is often a tedious yet monumental task, which requires significant efforts to manage the complexities as mentioned below:

Handling Heterogeneous Data Sources Drug discovery data is notoriously diverse, and encompasses structured and unstructured datasets from various experimental platforms. Ensuring consistency across such data is complex. Variation in data formats, measurement scales, and terminologies must be addressed to create unified datasets which can be effectively used for ML models.
Ensuring Data Quality Poor-quality data can severely compromise the outcomes of ML models. Missing values, errors, and inconsistencies are common in the process of drug development, and if unrectified, they can lead to unreliable predictions. Data Quality checks, rigorous data cleaning, and quality control measures, therefore, are integral to the harmonization process.
Scaling the Harmonization Process The sheer volume of data in drug discovery poses a challenge for harmonization. Manual harmonization is time-consuming and may not scale efficiently across large datasets. Automation can help, but balancing speed with the accuracy of data integration requires sophisticated tools and platforms.

Such challenges arise every day in drug development, where

Drug development relies on the seamless integration of genomic data, clinical trials and empirical evidence for accurate predictions.

As such challenges abound in the drug development process, there is a constant need for harmonizing the biological data, which meets quality standards and checks

Elucidata’s Data Harmonization Solutions

Elucidata’s data harmonization platform offers a robust solution to the challenges of data harmonization and capacitates organizations to utilize the full potential of machine learning (ML) in drug discovery.

AI-assisted Curation for Precision and Scale: Polly leverages a hybrid approach, as it combines automated AI-driven curation with expert human supervision. The platform's AI models automate the initial data curation process, which significantly accelerates data harmonization while maintaining high accuracy. Human curators then refine and validate the results to meet stringent quality standards. This dual approach ensures both scalability and precision in handling vast biomedical datasets. Polly can curate over 5k samples each week with more than 98% accuracy.
Efficient Integration of Diverse Data Types: Polly supports the harmonization of 26+ data types, and integrates them into a standardized framework with consistent terminologies. This allows ML models to consume data from multiple sources without requiring additional preprocessing, which, in turn, streamlines drug development pipelines. Polly's infrastructure also offers scalable storage and high-performance processing capabilities, and can efficiently handle large datasets. Currently, it processes more than 1 TB of biomedical data per week.
Modular and Customizable ML-ops Platform for Biomedical Research: Polly can serve as an end-to-end ML-Ops platform, simplifying the machine learning lifecycle from data ingestion and harmonization to model training and deployment. Each functionality is modular and allows users to tailor their experience in terms of building custom pipelines, utilizing data concierge services, or combining multiple features. Beyond data harmonization, Polly includes powerful tools for visualizing and analyzing harmonized data, which equips the researchers with knowledge and technical know-how to extract actionable insights.

Whether it's predicting patient responses to new treatments or identifying promising drug candidates, data harmonization plays a pivotal role in ensuring that machine learning models deliver reliable results. In the absence of harmonization, even the most powerful algorithms can struggle to make sense of fragmented data.

Real-world success stories like the rapid development of COVID-19 therapies highlight the positive and substantial role of harmonized data in advancing healthcare platforms provides the infrastructure needed to scale these efforts and support tasks like patient stratification, biomarker discovery, and target identification. Elucidata consistently creates and offers customized solutions for data harmonization using advanced technologies for researchers across the world striving for innovations in healthcare.