MorphoSys

Could you please walk me through the challenges faced while processing data?

As the head of biostatics and data management in an organization focused on oncology, ensuring data quality has been my primary focus because we are working with the lives of patients across the world. Data quality is one of the compulsory requirements of the pharma industry, and that is also one of the areas where we have put a lot of focus and energy. The data used in the industry for analysis is obtained from clinical trials conducted at different sites across the globe, making it difficult to ensure that the data entered is high quality. The biggest challenge is to ascertain whether the sites have entered all the expected variables, the data is clear, and conforms to stipulated guidelines, as most often during analysis, data issues surface.

What are the methods or frameworks employed to ensure data quality?

We are conducting clinical trials and providing patients the treatment through which we expect them to be benefitted. Consequently, when a patient drops out from treatment, it becomes a major concern. One of the data points that are very critical to us is finding the reason behind a patient backing out from the treatment. It can have multiple reasons, such as the doctor deciding to stop treatment, the patient having a disease progression, or the drug not working. We collect this data in a document called electronic case report form in electronic data capture systems. When a patient stops the treatment because of an adverse event, then there has to be a corresponding entry in the adverse event form.

We have data managers and medical monitors who are dedicated to reviewing the data. They check and validate the data entered from the sites, and our clinical research associates (CRA) visit the site for source data verification. The CRAs compare the doctors and lab reports present in the form of hard copies with the data entered into the system to improve data quality. In addition, we ensure data quality by programmatically analyzing the data to identify unusual data entries. For example, if a patient's weight is recorded as 25kg or 200kg, we've to make sure that it is not entered wrongly. These two values are improbable but not impossible, which is why validation becomes the key.

“Executive buy-in is one of the biggest barriers for companies trying to adopt a comprehensive eTMF solution. There has to be a top-down organizational commitment so all functional groups involved with the study makes it their objective to contribute to a complete, high-quality eTMF”

How is the pharma industry adapting to advanced technology?

In my understanding, the pharma industry takes a while to adapt to new technologies as intense regulations drive us. We have to go through a regulatory process to get approval before coming into the market. Unlike other industries such as banking or insurance, the immediate application of technologies like big data is not feasible for us. We are accountable for saving human lives, and our actions have direct repercussions on patients' lives because of which any technology or process has to be double-checked and validated before implementation.

What are some of the technologies that have been instrumental in helping your company achieve success?

Currently, we are using software like Spotfire for data visualization for reviewing the data and understanding and spotting patterns in them. Another issue we face as a global company is the heterogeneity in the data available because the methods of treatment in each country and physician vary. We have clinical studies that are happening in over 15 countries, which makes it further difficult for us to do the analysis. In order to mitigate this, we implement a standardization called central lab reading. The reports are transferred to the central lab in real-time, and two physicians are provided with access to read the data simultaneously. I identify this as one emerging field where real-time transfer of data using the latest technologies like artificial intelligence to read this data.

Is there any particular technology you would want to see applied in your organization as soon as possible?

The use of artificial intelligence to read the data in real time is one of the fascinating advancements that I look forward to. As an oncology company, the ability of AI to read tumor scans can help the physician instantly identify whether the tumor has progressed or shrunk or a new tumor has emerged somewhere else, which can fast-track the treatment process to a great extent. The employment of AI for the projection and prediction of patient enrolment is another advancement that we are keen on.

Any advice to your counterparts on how you can better streamline data gathering and analyzing processes?

The pharma industry is growing as a whole by people sharing lessons because we are dealing with real-life situations involving human lives. The unpredictability of the situations we are facing is forcing us to understand the probabilities and formulate plans to be equipped with solutions to face all the scenarios efficiently. Every company and team faces different challenges on a day-to-day basis and tackles them in their own way. The more people share their learning, their experiences, the challenges they face, and how they overcome them, the more helpful it would be for everyone in the industry. The sharing across the industry and partnering more with academic institutions will enable everyone to better understand the challenges and will ultimately prove helpful.