With the advent of pharma 4.0 and the ever-growing adoption of Industry 4.0 principles, there is a big push to make pharmaceutical operations more efficient, effective, and agile. The need for speed and agility is driven by the need to bring new drugs and therapies to market faster, to keep up with the increasing complexity of drug development, and to respond quickly to changes in the regulatory landscape.

To make this happen, pharmaceutical companies are turning to analytics to help them make sense of the huge amount of data they generate from their operations. Analytics can provide insights that traditional methods cannot, and this is giving rise to a new breed of data-driven decision-makers in the pharmaceutical industry.

In this article, we will explore how analytics is changing the way pharmaceutical companies operate and how it is being used to drive better decision-making across the entire value chain.

The Pitfalls of Traditional Approaches

The pharmaceutical industry has always been data-driven, but until recently, most of the data collected was used for compliance purposes. Regulatory requirements dictate that companies keep track of every batch of every drug they produce, and this data is typically stored in siloed databases.

The problem with this approach is that it is very reactive. Companies only have visibility into problems after they occur, and they are often not able to identify potential issues until it is too late.

This has led to a lot of costly recalls, as well as delays in getting new drugs to market.

In addition, the data collected by pharmaceutical companies is often spread across different departments and silos, making it difficult to get a holistic view of operations. This can make it hard to identify inefficiencies and optimize processes.

Analytics in Pharma

Analytics is the process of turning data into insights. It involves the use of statistical techniques and algorithms to extract meaning from data. Advanced analytics goes a step further by using artificial intelligence (AI) and machine learning (ML) to automatically identify patterns and trends in data.

According to Mckinsey & Company, applying cutting-edge RWE analytics across an entire value chain for in-market and pipeline items can result in more than $300 million in annual savings over the next three to five years.

Even though the adoption of AI/ML is still in its early stages in the pharmaceutical industry, a lot of companies are already using analytics and have seen significant benefits. From increasing the efficiency of manufacturing processes to reducing the time to market for new drugs, analytics is changing the way pharmaceutical companies operate.

What Does Analytics Add to the Picture?

The clinical value of data is not new. What is new, however, is the ability to analyze this data in real-time and at scale. This is made possible by advances in computing power and storage, as well as the development of new algorithms and statistical techniques.

Analytics can be used to identify patterns and correlations that would be impossible to spot with traditional methods. It can also be used to make predictions about future events, trends, and outcomes.

In addition, analytics can help to speed up drug development by providing insights that can guide clinical trials. It can also help to identify potential adverse events and monitor the safety of drugs post-market.

The new generation technologies not only help to integrate various business processes in a holistic manner, but they also aid in the development of compliance, trust, transparency, and pharma 4.0.

Four Main Categories of Analytics

Descriptive: Descriptive analytics is used to summarize data and understand relationships. It can be used to answer questions such as “What happened?” and “How did it happen?”.
Diagnostic: Diagnostic analytics is used to identify the root cause of problems. It can be used to answer questions such as “Why did it happen?” and “What can be done to prevent it from happening again?”.
Predictive: Predictive analytics is used to predict future outcomes. It can be used to answer questions such as “What will happen?” and “When will it happen?”.
Prescriptive: Prescriptive analytics is used to recommend actions. It can be used to answer questions such as “What should be done?” and “How can we do it?”.

How to Prepare Your Facility for Analytics

The speed at which organizations like AstraZeneca, Pfizer, Moderna, Merck, and Johnson & Johnson developed and delivered COVID-19 vaccines is remarkable. This innovation and deployment were possible due to the capabilities of AI and digital delivery at scale. It is now more important than ever for facilities to re-evaluate their readiness to move to Analytics and digitalize their organizations.

Here are some key considerations to keep in mind:

Identify the digital maturity level of your organization and decide where to focus your efforts, and set a roadmap for transformation. A digital plant maturity model (DPMM) can be helpful in this assessment.

Work with your team to evaluate space requirements for the new technologies and understand how the new technologies will impact existing processes and equipment.

Ensure your organization has the right skill sets in place, or plan to upskill employees as needed. A pharma facility with analytics will need to fill data analysts, data scientists, software developers, and other positions.

Develop a clear business case for the technology investments, with well-defined return on investment (ROI) targets.

Create a robust cybersecurity strategy to protect your data and systems from external threats.

Work with regulatory agencies to ensure compliance with all relevant regulations.

Evaluate your current network infrastructure and consider upgrading to a high-speed, low-latency network to support the new applications.

Consider potential risks and disruptions during the transition period, and put in place mitigation plans to ensure a smooth transition. Some of the common risks that you should assess include: Cybersecurity, Intellectual Property (IP), Data Privacy, Operational and Change Management.

Cost and schedule are crucial for the successful adoption of any new technology. It is important to have a clear understanding of the cost and schedule for each phase of the project. The plan should include funding resources, timelines, and milestones.

The move to analytics is a journey, not a destination. There is no one-size-fits-all solution, and the path forward will be different for each organization. The important thing is to get started and take the first steps on your journey to digital transformation.

Role of Analytics in the Pharmaceutical Value Chain

Drug Discovery and Development

Discovering any drug starts with disease understanding, wherein data from different sources is gathered and integrated to develop a hypothesis. This requires dealing with unstructured data like scientific literature, clinical trial reports, and genomic data. Analytics innovations can help researchers to develop a hypothesis faster by making connections that they might have missed. Predictive analytics can be used to identify the most promising candidates for clinical trials.

Once a candidate is selected for development, analytics can guide clinical trials by selecting patients most likely to respond positively to the therapy and by predicting which dosage will be most effective.

Analytics can also help monitor the drug’s safety by identifying potential adverse events and can forecast demand for the drug to ensure an adequate supply.

For instance, In this research, artificial intelligence software eToxPred was created to estimate toxicity levels of both synthetic and biological substances. Their AI model correctly predicted toxic properties in more than 72% of cases, with an overall error rate of just 4%. This accuracy potentially reduces the need for clinical trials.

Optimize and Improve the Efficacy of Clinical Trials

In the past, companies have had to rely on hypothesis-driven clinical trials, which can be time-consuming and expensive. Now, with data analytics, it is possible to conduct “virtual clinical trials” that are powered by data from multiple sources.

This approach can help reduce the number of patients needed for a trial, as well as the time and cost. It can also help identify the most promising drug candidates, monitor the safety of drugs in development, and predict how patients will respond to treatment.

Quality Control

After the products have been manufactured, they go through a quality control check. This is to ensure that the products meet the required standards and are safe for use. Quality control is an important step in the manufacturing process, as it can help to prevent accidents and injuries. Regular Surveillance of data can help identify any issues with the product before it reaches the market.

Regulatory Compliance

In the pharmaceutical industry, companies must comply with a variety of regulations, including those from the FDA. Data analytics can be used to monitor compliance with these regulations. For example, by analyzing data from clinical trials, it may be possible to identify when a company is not following the protocol and needs to make changes. Additionally, by analyzing data collected from the manufacturing process, it may be possible to identify when a company is not following good manufacturing practices and needs to make changes.

Better Insights into Patient Behavior to Enhance Drug Delivery

Technology is giving pharmaceutical companies greater insight into patient behavior. This data, when coupled with analytic models, can help design services targeted to different demographics or at-risk patient groups. This can improve the efficacy of treatment and patient outcomes.

Improving Sales and Marketing

Sales and marketing teams working in the pharmaceutical industry have always been under pressure to improve their performance. Data analytics can help them to identify new opportunities, optimize their sales strategies, and track their progress.

Additionally, data analytics can help companies to understand how physicians make prescribing decisions and what factors influence their decisions. This information can be used to develop targeted marketing campaigns that are more likely to be successful.

Benefits of Implementing Analytics to Pharmaceutical Operations

Identifies and Mitigates Risks related to the development, production, and distribution of pharmaceutical products.
Leverages data to assess the quality of products and ensure regulatory compliance.
Enhances the efficacy and efficiency of clinical trials.
Improves quality control and the management of inventory and logistics.
Helps companies keep up with changing customer demands and establish powerful marketing strategies.
Reduces the time and cost of product development.
Increases the chances of success for new drugs and therapies.
Helps pharmaceutical companies gain a competitive advantage in the market.
Helps to protect patients and consumers by ensuring that only safe and effective products are made available.
It can be used to support environmental sustainability initiatives by reducing the wastage of materials and energy during production.

Data analytics in the pharmaceutical industry is poised to benefit greatly from its adoption. Embracing data analytics will help pharmaceutical companies improve their operations and better serve their customers.

If you are just starting to work with analytics in your organization or are looking to scale your analytics capabilities, consider working with our data analytics consultants. We have experience across various industries, including pharmaceuticals, and can help you strengthen your data analytics program.

Request a consultation today to learn more about how we can help you.

Changing the Way, We Look at Pharmaceutical Operations With Analytics