The Impact of Big Data Analytics on Pharma Industry

The pharmaceutical sector generates enormous data, from clinical trials and EHRs to genomics and patient-reported outcomes. Big data in pharma serves as a key factor in keeping it all together as it streamlines drug development and uses genomics for personalizing treatment and making clinical trials faster and more precise.

Without big data, drug development in pharma was a slower, more trial-and-error-driven process. Researchers relied heavily on limited datasets from lab experiments and clinical trials, making identifying precise drug targets difficult. Every process was slower, more expensive, and with a higher failure rate. The tides turned for big data in pharma around the early 2010s, as processing capabilities and machine learning technologies improved and became more accessible.

A critical turning point was during the COVID-19 pandemic when big data played a critical role in accelerating vaccine development. Today, big data analytics in pharma accelerates and enhances drug development in every medical field, including for rare conditions and conditions with high mortality rates. This article reviews how big data helps in drug research and development, clinical trials, individualized treatment, and post-market drug monitoring.

Big data in research and development of new drugs

Big data in pharma plays a crucial role in accelerating the development of new drugs by enabling advanced analytics. Here's how it is used:

Genomic data analysis

Big data analytics in pharma allows for processing vast genomic datasets and helps researchers identify genetic markers and variations associated with diseases. By understanding these genetic patterns, scientists can design targeted therapies, predict patient responses, and enhance the effectiveness of drug development. Genomics data analysis is critical at the initial stage of drug discovery and during drug development with real-world data .

Examples:

• A modern example of using Big Data in genomics data analysis is the development of CAR-T cell therapy for cancer treatment. This therapy, used in blood cancers like acute lymphoblastic leukemia (ALL) and diffuse large B-cell lymphoma (DLBCL), involves genetically modifying a patient's T cells to target specific cancer cells. Researchers use multidimensional omics data (including genomics, transcriptomics, and proteomics) to understand the molecular behavior of CAR-T cells in the body. This integration of large datasets enables the discovery of new tumor targets, identifies resistance pathways, and improves the therapy's overall design and effectiveness (Source ).
• Genomics data analysis was used in the research and development of new drugs even before the widespread adoption of big data. For instance, it was used for the development of the drug Herceptin for breast cancer treatment. Researchers analyzed vast amounts of genomic data to identify the HER2 gene overexpressed in some breast cancers. By targeting this gene specifically, they developed Herceptin as a targeted therapy, which helped to improve outcomes for patients with HER2-positive breast cancer (Source ).
• Zelboraf (vemurafenib) was developed to treat melanoma based on genomic data showing that approximately 50% of melanomas have a mutation in the BRAF gene. By targeting this specific mutation, Zelboraf offers an effective treatment option for patients with BRAF-mutant melanoma (Source ).

Modeling biological processes

Through advanced computational models and simulations, big data for drug development can replicate biological processes at a molecular level. This capability helps researchers understand how potential drug compounds interact with biological systems, predicting their behavior before proceeding to lab experiments or clinical trials. It saves time and resources in the drug discovery phase.

Examples:

• Big data is used in the pharmaceutical industry to model protein folding, as seen in projects like AlphaFold. By analyzing vast datasets of protein structures, AI-powered tools predict how proteins fold, helping researchers understand the molecular mechanisms of diseases and identify how drugs can interact with these proteins effectively. This helps in developing drugs for cancer and neurodevelopmental diseases (Source ).
• During Pfizer's development of the COVID-19 mRNA vaccine, big data was crucial in simulating immune responses and predicting the behavior of the spike protein of the SARS-CoV-2 virus. Researchers used models based on massive data to optimize the vaccine's design before entering clinical trials. This allowed them to adjust the vaccine to improve its efficacy against emerging variants​ (Source ).

Predicting drug effectiveness

Predictive analytics tools use historical and real-time data to forecast the effectiveness and safety of new drug compounds. By leveraging data from previous clinical trials, genetic studies, and patient health records, pharma big data analytics helps anticipate how a drug will perform in different populations and conditions, leading to more efficient drug development processes and reducing the likelihood of failure in later stages.

Computer modeling is ethical and more accurate than traditional animal testing. Processes like pharmacokinetic modeling help understand how drugs behave in the human body.

Examples:

• Novartis uses big data to assess the effectiveness of its CAR-T cell therapy for cancer treatment by analyzing data from past patient outcomes and genetic profiles. This predictive analysis allows the company to tailor treatments for specific patient groups and optimize therapy protocols for better results (Source ).
• Novo Nordisk utilizes Big Data to predict patient responses to its diabetes medication, Ozempic (semaglutide). By analyzing health records, blood glucose levels, and genetic markers from thousands of diabetes patients, the company tailors dosing recommendations and identifies subgroups likely to benefit the most from the medication (Source ).

Optimizing clinical trials with big data

• Big data in the pharma industry helps refine participant selection for clinical trials by digging into massive datasets like EHRs, genetic data, and lifestyle info. By looking at specific factors such as genetic mutations or a patient’s medical history, researchers can handpick candidates more likely to benefit from the treatment. Gene expression is a crucial factor for target selection. For example, suppose a trial targets a drug that works on a particular gene variant. In that case, the system filters for individuals carrying that variant, making the recruitment process faster and the trial’s chances of success higher.
• Big data and analytics for pharma enable the real-time monitoring and analysis of clinical trial data. For example, in managing multiple sclerosis (M.S.), Novartis uses Big Data analytics to track patient responses to its drug Gilenya (fingolimod). Real-time monitoring of patient MRI scans and other biomarkers allows for rapid adjustments to the trial protocol if adverse effects are detected or if certain subgroups respond differently (Source ).
• Big Data improves clinical trial precision by combining diverse data sources like genetic information, patient-reported outcomes, and data from wearables. This holistic approach offers a clearer understanding of how treatments work across varied populations. For instance, Johnson & Johnson uses Big Data in trials for Stelara (ustekinumab), a treatment for Crohn’s disease, to analyze genetic data, symptoms, and microbiome information. This detailed analysis helps them fine-tune dosages and tailor treatments to different patient subgroups. Clinical studies have shown that patients using Stelara as a first-line treatment spend significantly more time in remission than those on second or third-line treatments (Source ).

Using big data for individualized treatment approaches

Personalized treatment is the future of medicine that can help solve the most complex issues, like different responses of patients with the same type of tumor to chemotherapy. Digital therapeutics and pharma are leveraging big data to create personalized treatments based on patient-specific data. Here is how:

1. Bringing different data together:

Big Data pulls information from various sources like genetic tests, electronic health records (EHRs), fitness trackers, and environmental factors. This combination creates a full health profile for each patient. It helps doctors better understand the unique factors, such as genetics and lifestyle, that affect an individual's health and disease risk.

2. Predicting health risks:

With big data analytics in pharma, doctors can look at a person's genetic and medical history to predict their chances of developing conditions like heart disease, cancer, or diabetes. This allows for early intervention and more personalized prevention plans and improves health outcomes over the long term. Big data also helps predict the effectiveness of different treatments.

3. Designing targeted treatments:

By identifying specific genetic mutations or disease markers, researchers can develop treatments that target those exact traits. This is especially useful in cancer treatment, where drugs can be designed to match the genetics of the tumor.

4. Assisting in clinical trials:

Big data analytics in pharma is crucial throughout the entire duration of clinical trials. First, it helps recruit candidates faster by acquiring relevant data about them from available sources. This is especially crucial for clinical trials for rare conditions, as finding candidates without big data is often nearly impossible.

Pharma big data also helps skip control groups with data analytics in clinical trials , as virtual control groups based on past trials can be generated instead. Finally, big data analytics also inform the control of clinical trials throughout their duration.

Successful cases of big data in personalized medicine

• Oncology:

Platforms like Oncora Medical integrate EHRs, radiology reports, and pathology data to help oncologists personalize cancer treatment plans. For example, using this platform, the MD Anderson Cancer Center improved efficiency by reducing data entry time by 67% (Source ).

• Diabetes management:

Big data analytics have been used to model the progression of type 2 diabetes, incorporating information from wearables and genetic data to create personalized interventions. This approach allows healthcare providers to predict complications and provide personalized interventions to diabetic patients. For example, the Rothman Index developed by PeraHealth uses patient data to provide real-time monitoring and has significantly reduced mortality rates from conditions like sepsis (Source ).​

• Neurology and Alzheimer's disease:

In neurology, Big Data has accelerated drug development and personalized treatment plans. For instance, Biogen utilized an AI-driven Big Data platform to analyze clinical trial data, predict outcomes, and adjust strategies accordingly, which can prevent costly failures and refine treatment protocols for Alzheimer's disease.​ This approach was particularly evident in their BIIB080 trials, where data-driven analysis allowed for better management of patient's responses to treatment targeting the tau protein, a key biomarker in Alzheimer's disease (Source ).

Post-marketing drug monitoring using big data

Pharmaceutical companies utilize big data in pharma for post-marketing drug monitoring (also known as pharmacovigilance) to track the safety and effectiveness of medications after they enter the market.

When pharmaceutical companies and agencies like the FDA work with Real-World Data (RWD), they gather information from electronic health records (EHRs), patient registries, and wearable devices. These data sources are collected into central platforms where the data is standardized and cleaned up to be analyzed effectively. Tools like the FDA's Sentinel System use this data to monitor how drugs perform in real-world environments, tracking patients' health outcomes and any side effects in real time. This way, drug safety is monitored continuously, and any issues outside clinical trials can be caught early.

For example, when companies noticed cardiovascular risks tied to JAK inhibitors used in treating autoimmune diseases like rheumatoid arthritis, they analyzed vast datasets of patient health records and other real-world data. The patterns they found helped flag the connection between the drug and increased heart issues, which led to safety updates and new treatment guidelines.

Big data analytics in pharma are driven by machine learning models and data mining that look for unusual patterns or spikes in adverse reactions. AI in pharma R&D is also common.

Pharma big data also helps with large-scale analysis of social media comments for adverse drug effects. One example of big data that helps with the large-scale social media analysis of adverse drug effects is the FDA's OpenFDA initiative. It uses social media platforms like Twitter to monitor patient-reported adverse drug reactions. The system can analyze millions of posts to detect early warning signs of medication issues.

The challenges of using big data in pharma

Managing big data in pharma is a risky and resource-consuming process. Here are the challenges you might expect:

• Data sources standardization and integration

Data in the pharmaceutical industry comes from diverse sources such as electronic health records (EHRs), clinical trials, genetic studies, wearable devices, and registries. These data sources often use different formats and standards, making it challenging to integrate them into a unified platform for analysis​.

The need for standardized data formats and protocols further complicates data integration. Disparate healthcare systems and data silos can hinder the seamless exchange of information, slowing down the efficiency of big data applications in drug development and patient monitoring.

These challenges call for end-to-end data integration, starting with collecting data, connecting all data sources, and implementing QA practices. Binariks can assist you with these tasks and more.

• Data accuracy

Healthcare data quality varies widely, as errors and inconsistencies in EHRs or patient self-reported data can compromise the reliability of big data analytics in the pharmaceutical industry. Data must be cleaned and validated for successful analysis, which requires significant resources​.

At Binariks, we can develop custom algorithms and data pipelines that automatically identify and correct errors. We can also standardize data formats and ensure compatibility between various data sources.

• Organizational issues

In the pharmaceutical industry, different teams are traditionally responsible for various systems and data sets. This can hinder digital transformation in pharma if teams are not on the same page.

An example of different teams managing separate systems in the pharmaceutical industry is in clinical trials. The clinical development team may handle trial design and patient recruitment data, while the regulatory affairs team manages submissions and compliance documentation using different platforms.

At the same time, the pharmacovigilance team might track adverse events through a separate AI in the pharmacovigilance database, leading to silos that complicate data integration across the organization. These issues are solved by adopting a data-centric approach in which each piece of data has a clear owner.

• Regulatory compliance

Big data in pharma must comply with regulations like GDPR (General Data Protection Regulation) in Europe and HIPAA (Health Insurance Portability and Accountability Act) in the U.S. FDA requires software used in the pharmaceutical industry to comply with many requirements, including access control, ID verification, and others.

Ensuring data is anonymized and securely stored is essential but challenging due to the volume and complexity of healthcare data. Breaches in data security can result in severe legal and financial repercussions for pharmaceutical companies​. Binariks can assist you with the main components of regulatory compliance, such as data encryption, cloud storage, and automatization.

• Lack of talent

The pharmaceutical industry faces a shortage of professionals skilled in big data analytics solutions. Working with big data requires cross-disciplinary bioinformatics, programming, and data governance expertise. Pharmaceutical companies often need to invest heavily in training or collaboration with tech firms to bridge this gap​. Hiring skilled external teams like Binariks is an excellent solution that cuts costs compared to creating an in-house team.

Final thoughts

The benefits of pharma big data analytics are immense. Big data uses genomic data to accelerate drug discovery and development, allows for more precise recruitment of trial participants, facilitates the development of personalized medicine, enhances pharmacovigilance, and optimizes manufacturing. In less than ten years, big data completely transformed the pharma industry and will continue to do so.

McKinsey estimates that applying big data strategies can generate up to $100 billion in the pharma industry in the upcoming decade. What's next for big data in pharma? We can expect predictive modeling of biological processes and drugs to become more advanced and common. The quality of clinical trials will also improve with a more precise selection of participants and better monitoring. Big data in pharmacy will be crucial for new drugs like personalized vaccines for cancer, gene-editing therapies like CRISPR, and the next pandemic.