How Data Science Consulting Helps in Medical Research and Drug Discovery

There has been considerable progress in the healthcare industry, and much of this credit goes to advancements in medical research and drug discovery methodologies. If you witness the global trend, drug and pharmaceutical companies have expedited the post-pandemic research and discovery process. Yes, we must thank digital transformation technologies like artificial intelligence, machine learning, the Internet of Things, and robotic process automation for revolutionizing medical research. Still, there is one more innovative trend that has equivalently contributed to bringing this paradigm forward. 

We are talking about data science consulting. Data science has brought a significant transformation in how different industries and businesses work. However, its impact on healthcare has been huge. This is because healthcare and medical science generate large volumes of data during their groundbreaking discoveries to evolve treatment methods and therapies. Using data science consulting services, organizations can simplify complex data processing and management with complete analysis to gain actionable insights. 

In this blog, we will find out how data science consulting is reshaping drug discovery and medical research using real-world examples and case studies that highlight its significant impact. 

Accelerating Drug Discovery with Predictive Modeling

There was a time when drug discovery was considered an expensive affair and a time-consuming process because scientists needed to get all the chemical compositions right. The result was it took years to release a new drug or vaccine to the market. However, data science has changed this scenario completely and helped pharmaceutical companies accelerate their processes by implementing predictive modeling. 

Here, we can highlight the example of Insilico Medicine, which implements artificial intelligence and machine learning algorithms to predict the potential success of drug composition. Predictive modeling allowed the company to minimize time for identifying promising molecules. 

Enhancing the Clinical Trials Using Data Analytics 

Clinical trial management is an integral part of the drug discovery process. However, it was also suffering from inefficiencies before data science consulting stepped in to optimize the process with advanced data analytics solutions. One of the best visible outcomes is reducing the cost of trial maintenance over long periods and streamlining the patient recruitment process. 

Data science has played a pivotal role in optimizing trial design, patient selection, and thorough analysis. Data analytics tools can quickly identify relevant patterns and real-time insights to improve the trial success rate. In addition, natural language processing analyzes electronic health records (EHRs) to find out the most ideal candidates for clinical trials based on past medical conditions, treatment, and responses. 

Let’s discuss the Pfizer case study where the pharmaceutical company employed machine learning algorithms to select patients for a breast cancer trial. Not to say that data science played a comprehensive role in designing the trial and choosing the patient appropriately with more chances of driving success rate. It also enabled the company to predict patient responses, speeding up the process and reducing trial duration.

Optimizing Drug Repurposing

Have you heard about drug repurposing before? It is the advanced process of discovering new use cases for existing drugs and plays an essential role in significantly reducing drug development costs and time. Now, data scientists are helping pharmaceutical companies harness genomic information, and analyze medical literature and historical clinical data to discover drugs that can be repurposed for different medical conditions. 

Not to forget that machine learning algorithms also assist drug companies in finding out the relation between diseases and existing drugs that were not apparent before. This is the core reason why drug manufacturers are partnering with leading data science consulting firms to acquire the necessary technical skills and knowledge to build and deploy these models, guiding them to make data-driven decisions for drug repurposing efforts.

For instance, the drug Colchicine, which is usually prescribed as an anti-inflammatory medicine for gout was found to contain potential benefits to treat COVID-19-affected patients. It was a data-driven and thoroughly researched discovery based on real-world use cases and clinical trials leading to drug repurposing. 

Harnessing Genomic Data for Personalized Medicine

You will probably agree that medical research is exploring new avenues these days with personalized medicine, customized treatment, and genomic data gaining more visibility. Especially, if we talk about genomic data, it has brought a revolutionary change in research where analyzing vast and complex pools of data has become streamlined.  

But you need to remember that data science consulting is the main force empowering genomic data and allowing researchers to find biomarkers linked to specific diseases and treatment responses. Also, it receives ample support from AI/ML algorithms in forecasting how patients will respond to particular drugs and strategizing plans for their personalized treatments. 

We thought that the GNS Healthcare case study is quite relevant to put up here. The company employs data science services to enhance personalized medicine and customize treatment plans with the help of genomics data based on the patient’s profile. It has allowed the healthcare firm to boost efficiency and minimize adverse reactions. 

Improving Patient Outcomes with Real-World Evidence (RWE)

Data science consulting is transforming medical research and drug discovery with real-world evidence or RWE — a process in which it gathers instant data from real-world sources like EMR and EHR software, insurance claims, and patient registers. The main purpose of RWE is to enhance patient outcomes and provide improved treatment procedures. 

RWE enables pharma companies to track and evaluate long-term drug safety and efficacy that are available in the market. Doctors and healthcare professionals can make more informed decisions related to treatment strategies.

During our research, we came across a use case for Flatiron Health, which revealed that the healthcare technology company, uses RWE to improve cancer treatment outcomes. It analyzes data from multiple sources and provides cancer specialists with accurate and real-time insights to make the best treatment decisions.  

Identifying Drug Targets Using Machine Learning Algorithms 

As you already know data science solutions and machine learning algorithms are correlated with each other. So, this viable combination allows pharma companies to identify the drug targets precisely using machine learning models. Machine learning modules can easily and quickly identify large biological datasets to discover proteins, genes, or other biological entities that are associated with specific diseases.

All these useful insights and research allow companies to focus on developing drugs that can provide optimal results while treating a disease or medical condition.  In addition, consultants may use unsupervised learning techniques to discover previously unknown patterns in genomic or proteomic data, leading to the identification of novel drug targets.

We can highlight the example of DeepMind’s AlphaFold, which created a significant breakthrough in 2020 by solving one of the most challenging problems in biology—protein folding.

Reducing Costs with Automation and AI

Did you know that according report published by the National Library of Medicine, the cost of developing a new drug ranges between $314 million to $2.8 billion? Well, that’s a whooping figure and hence all pharmaceutical companies are looking for viable and optimal solutions to curtail this exceeding cost. The good news is that data science consulting firms are lending their full hand support to this mission with their expertise in artificial intelligence services. 

Data science and AI-powered algorithms automate the different drug discovery stages from data gathering to trial monitoring. Automation enhances efficiency and accuracy in workflow, thereby eliminating all possibilities of human error in analyzing data.  

Let’s take the example of Exscientia, a UK-based AI-driven drug discovery company that uses AI to automate the design of drug candidates. 

Conclusion 

Data science consulting has taken the life science industry to the next level with sheer transformation in medical research and drug discovery. From predictive modeling and patient selection to personalized medicine and cost reduction, data science is driving faster, more accurate, and more efficient drug development processes. 

As the volume of healthcare data continues to grow, the demand for expert data science consulting will only increase, enabling pharmaceutical companies to unlock new treatments and improve patient outcomes. 

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