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Post-approval service providers must collect less but more focussed data, says INC

By Natalie Morrison

- Last updated on GMT

Post-approval service providers must collect less but more focussed data for successful drug launches, according to INC Research.

The firm told that many post-approval studies are simply repeated Phase III protocols with “some health economics questionnaires thrown in for good luck.”

But with an increasing focus on “real world data”​ from post-approval studies – and a growing late phase client list for INC – the firm believes the model is in desperate need of improvement to become more efficient.

Sally Osmond, executive VP and general manager of clinical development, post approval and strategic services at INC told us: No matter how well you deliver a clinical trial or programme, and no matter how robust the programme, it’s only a snap shot of the real world, of the patient and how the healthcare has been carried out.

“The minute the drug is released onto the market place it will be used in a broader sense. It will be used in different environmental health ways, different groups of patients, for instance patients may be less compliant than in a clinical trial setting. The truth is clinical studies can only give you so much”

She urged the industry to increase post-approval vigilance and to look for “signals”​ to avoid instances where widely used medications have been found in-efficious or dangerous two to three years after release.

“It means we must do more post-approval work look at the level of real life real populations and real prescriber. If there are signals, pick them up earlier, do something about it.”

How to improve data collection

Osmond told us the market must start by collecting the right kind of data, and avoiding the high levels of repetition common in the sector today.

She said: “If a product has already been approved you don’t need to do the Phase III study again and people are slowly realising that.”

The change, she added, must ultimately come from early planning stages, where teams should ask themselves “what are we doing this project for?” ​from the onset.

“If you ask yourself at the very beginning, what is the output going to be used for, it allows you to work through that paradigm, and will help avoid recreating data.

“You do a much more focussed job for a reduced price, and you will not sacrificing quality. What you’re sacrificing is data you didn’t need in the first place. We’ve continued to collect it because of laziness, and the attitude that ‘it’ll be alright we’ll keep doing the same.’”

The pay-off for the extra effort, she added, will be worth it especially in the current growing global economy by which more emerging markets are now able to afford access to medicine.

“I think the other thing is that as we see the global economy growing the introduction of a new drug is going to much more global,” ​she said.

There will be much more post approval interest as you introduce a new molecule to various genetic groupings and healthcare types, and you need to keep a track across all of those to see if there’s going to be any variability to what you had surmised from your pre-approval data.”

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