Big Pharma & Big Data: A quick study...

Growth & Demand

Pharmaceuticals are growing at an accelerated pace in the current market. It was estimated to be worth $1170 billion in 2021, CAGR 5.8% from 2017. Data science has played a pivotal role in this growth. Due to the accumulation of huge medical and pharmaceutical data, data science has gained a great deal of traction in the pharma industry. In order to gather, normalize, analyze and harness the vast amounts of data that fuel modern medicine, AI capabilities such as data integration, pattern recognition, and evolutionary modeling are essential. From finding a trial to enrolling patients to ensuring adherence to medication, it may disrupt every part of the clinical trials process. The use of machine-learning algorithms can optimize clinical trial research in many different ways. Pharmaceutical companies have begun implementing data science techniques to optimize processes and improve results. As data science technologies for pharma – including cloud computing, machine learning, and so forth, are continually evolving, the pharma data analytics industry promises several cutting-edge innovations to assist in formulating a fact-based strategy within the global market. As a result, drug development time is shorter, costs are saved, and pharma demands are predictably forecasted.

Applications

Here is a quick study of how data science is revolutionizing the pharmacy industry.

Drug discovery and development:

Pharmaceutical professionals can use data science and automation to identify drug candidates for trials by screening millions of compounds. Through data science, it's also easy to automate the process and it can dramatically speed up the process of discovering drugs and developing them.

• Deep Chem : Through DeepChem, scientists strive to democratize drug discovery through the use of open-source toolchains that use deep learning. To find a suitable candidate for drug discovery, the model uses Python-based MLP.
• Deep Tox : With DeepTox, more than 12,000 environmental chemicals and drugs are predicted to be toxic through 12 different tests. 
• DeepNeuralNetQSAR : Drug discovery is routinely aided by Python-based quantitative structure-activity relationship models (QSARs). In QSAR, the molecular structure is used to predict the biological activities of molecules.
• ORGANIC : It is a molecular generation tool that helps to create molecules with desired properties.
• PotentialNet : PotentialNet is used for Molecular Property
•  Hit Dexter : It is a machine learning approach to estimate how likely a small molecule is to trigger a positive response in biochemical assays.
• DeltaVina – It is a scoring feature for rescoring protein-ligand binding affinity
• Neural graph fingerprint: These are useful for predicting the properties of novel molecules
• AlphaFold: - AlphaFold is an AI system developed by DeepMind that predicts a protein's 3D structure from its amino acid sequence.
• Chemputer : A radical new method of producing drug molecules, which uses downloadable blueprints to easily and reliably synthesize organic chemicals.

Optimize and improve the efficacy of clinical trials:

With the help of big data, pharmaceutical companies can analyze past demographic data, behavior and condition records, and previous clinical trials. Predicting potential side effects and preventing them in advance is possible. The use of advanced predictive analytics can enable researchers to identify potential candidates for clinical trials based on a wide range of information, including the presence of social media profiles, interactions with doctors, and genetic data that relates to a certain group of people. The cost of recruiting clinical trial subjects can be enormous, and AI solutions can dramatically reduce this expense. AI can also be used to improve the safety of clinical trials, which is another burgeoning application. Remote monitoring and the access to real-time data enable researchers to more accurately monitor biological changes, as well as identify if a participant is adversely affected by treatment.

Personalize & Create Targeted Medications:

It is crucial to personalize medication plans to process and integrate a huge amount of data from a wide variety of sources. In personalized medicine, therapies can be tailored to maximize patient response and safety margin so that better patient care can be ensured. Personalized Medication holds promise for improving health care by allowing each patient to receive an early diagnosis, an accurate risk assessment, and optimal treatment. Data science technologies are ideal for this. When combined with genomic sequencing, the patient's medical sensor data, and their medical records, these technologies enable personalized medication plans to be developed. 

Improve drug delivery and effectiveness and healthcare outcomes:

With genome sequencing, electronic medical devices, and remote sensor devices - and advanced analytics - pharmaceutical companies can collect larger amounts of data about patient behavior. The company can tailor its services based on that information to target different demographics or at-risk patient groups and increase the effectiveness of treatments.

Gain improved insight into marketing:

With improvements to personalized medication plans, niche markets are increasingly important for pharmaceutical companies. Pharma companies can use data science to detect underserved markets and analyze them further, coming up with solutions for the disadvantaged. Based on market insights, companies are able to develop strategies for promoting their products. A market insight tool can help them find market coverage for their product. Market access with drug coverage, geo-mapping of coverage territories, launch coverage of new to market drugs, and payer landscape profiling are some of the key applications consumed by Big Pharma in drug commercialization process. 

Challenges

As happens with the adoption of new tech, the growing use of data science in pharma has surfaced several new challenges through the ecosystem.

Technology and analytics:

Pharma companies are now burdened with outdated systems containing heterogeneous and disparate data. It is essential to rationalize and interconnect these systems so that data can be shared. Additionally, it is difficult to find people with the experience and training to develop the technology and analytics needed to maximize the value of the existing data.

Mindsets:

Most pharmaceutical companies believe that improving big-data analytical capabilities is not worth the investment unless an ideal future state is identified. Interestingly, there are a few examples of pharmaceutical organizations taking advantage of data science in a way that generates a lot of value, since they are cautious about being first. Moreover, they are hesitant for fear of increased regulatory interaction if they undertake a big-data change initiative

Outlook

Data science has become a vital part of today's pharmaceutical industry. This is because it enables the clients at the end of a pharmaceutical flow to gain a better understanding of new therapies which will be released on the market. It also provides a better understanding of the average performance of the drug in the market, which is vital for the promoters. New drug therapy can also be developed based on the performance of the drugs currently on the market and the performance of the drugs already introduced. Increasing pharmaceutical efficiency and innovation is essential. With help of data science in pharma, there is a bright future for precision medicine, decreasing drug failure rates or lowering research and development costs. In an age when data is the new oil, pharmaceutical companies must leverage this resource to provide better and more efficient medicines to humanity.

The Agilité Connection

Here at Agilité, we are focused at assisting our customers to get the maximum value possible out of their data. As the leading technology services provider for market access data, analytics, and insights, we support our clients by providing clarity and confidence to make better decisions. Using technologies for gathering and processing unstructured natural language formatted raw data, we create meaningful, actionable insights for our industry partners. For our clients, we develop cutting-edge analytics systems utilizing data science, NO-SQl data lakes, and BI tools that will enable them to gain important insights for crucial decision-making and formulating new strategies. We serve our clients by injecting advanced analytics tools into existing systems helps to creates more accurate clinical and business assessments in less time, producing immediate positive impacts on work efficiencies and revenue.

References

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