Drug development productivity continues to fall, yet the pressure is rising on teams to find new insights at a faster pace. This is a difficult task when the data needed to drive discovery is siloed and one dimensional.
In this interview, April Pisek and Unjulie Bhanot, Solution Owner (Biologics Development) from IDBS all share the challenges impacting drug development and discovery, how to map out the laboratory landscape and what to consider when transitioning to an automated system.
Deep Dive: Join Unjulie for her webinar at SmartLab Digital 2020. Secure your place here
Pharma IQ: What do you see as some of the biggest challenges people are facing during the drug development phase?
Unjulie: In both the Pre-Clinical and Bioprocess Development space, the focus is on reducing a drug’s time to market and making sure the drug is doing what it is meant to be doing; the drug needs to be effective, potent and aimed at the right target. Organisations will also be considering profitability versus operational costs and regularly reviewing how much regulatory scrutiny they are facing.
From a scientist’s perspective in the lab, they may be facing challenges from using manual or paper based processes - seeing things in a very one dimensional format and they may be struggling with gathering relevant data fast, preventing mistakes where possible and tracking mistakes that are inevitably made.
Some of their tasks may also be performed for the sake of compliance rather than supporting the science that is being done in the lab.
Challenges may include; one dimensional data, tasks which don't support the science, siloed data and outdated technology
Scientists may also be using outdated technology. If they (the instruments) are referenced on a patent or a drug filing document they may need to continue to support and maintain their kit, even if it is not performing in the same way as when it was purchased.
Another consideration is siloed data, they (scientists) may be facing the fact data lives physically in different space, in lab books, different excel sheets, different proprietary formats. This can cause a loss of insight into the data, along with delayed identification of issues and difficulties with collaborating.
April: One thing I’ve heard a lot of customers say is a big challenge for them is access to all of their data for collaboration.
Some may be using paper notebooks and Excel while storing information on their laptops and some on network drives. This can make it very difficult to find data, report on the multiple data sets and collate the data to achieve the higher level of insight required to understand if a treatment is as efficient across multiple studies, for example.
A lot of customers struggle with access to their data for collaboration
The other thing that I’ve noticed is that when you have multiple sites in the company, it is very hard for them to be consistent with their naming conventions. That can lead to a mismatch of the data if people do not realize that they have used a different name.
I’ve also seen a lot of customers wanting to integrate with multiple external systems. They may have a LIMS, other laboratory software, and instruments with additional software that are all processing their data. This means multiple places for storing data, so bringing everything together is definitely a challenge.
Find out more: Discover how to bring your data together in one system
Pharma IQ: What are some of the short term and long term impacts of these data inefficiencies across the drug development process?
Unjulie: In terms of short term impact, you will see reduced productivity, employees having to do re-work, late identification of issues and compliance failures. On a larger scale, you may then get compliance queries from regulators and customers and you can be in a very tough situation when it comes to audits.
In the short term, you will see reduced productivity, re-work, late identification of issues and compliance failures
Also, if you can’t deliver your work on time, that can have a direct impact on the company’s bottom line. This is particularly true for CROs as that may cause delays in payment. In the CMO and CDMO space you may also develop an unfavourable reputation in the market and poor credibility if (potential) customers find out you’ve had compliance queries or issues in the past.
Long term, it can have a big impact if you can’t get your data out to your customers.
If you’re a CRO, for example, then the sponsor Pharmaceutical company will need data from you to move a drug to the next stage. If a CRO cannot share the relevant data in a timely fashion, they are disappointing the pharmaceutical companies and stopping themselves from being able to move on to their next project.
April: Short term, consider the cost of the resources used every day and time spent on research that ends up unsuccessful that could have been prevented if insights were learned earlier in development. The capability to query across multiple systems and collate data in the exact format to determine the success of a new treatment becomes imperative.
Long term, it could mean repeated experiments, loss of data, data integrity issues, or even unfortunate FDA findings, as well as, the potential prevention of necessary drugs getting to the market to help diseased patients.
Unjulie: If people are hearing negative comments, they may also start to question the quality of the product that you are making. For CROs and CMOs, they may wonder if the testing or manufacturing is reliable. You can’t prove things if you aren’t documenting and consolidating that information and that can raise questions in the market.
Pharma IQ: What is the solution for addressing this type of problem?
April: By using a single platform to encompass all the different business and laboratory areas, or a tool that has the capability to pull data from multiple systems.
If the data is stored in one system, it is much easier to retrieve than if it was stored over multiple systems. Being able to have everything the lab needs to operate, in one system, can help you create efficiencies, prevent mistakes, reduce re-work, increase data integrity, report on data faster, run more samples, and perform comparisons that lead to real insight.
Unjulie: It’s important to think about the laboratory landscape as a whole and map out what it is you want to deliver. This way you can implement a solution that can deliver more than one functionality to different factions of the business.
Make sure to map out the laboratory landscape as a whole so you can really understand what it is you want to deliver
You have to think about the scientists entering their data at the point of running an experiment, where that data may have come from (for example reagent preparation performed by someone else in the lab), software used to process and analyse the data, and then the consolidation of this data and compilation of reports. These are all elements than play into the overall role of the business so finding a streamlined solution is quite important.
Organisations also need to look into moving away from paper. It is no longer a reliable and effective way to report data and pull it all together. This also links to out-dated technology, such as old systems which generate print outs, as these are not really conducive to the way people work now.
Pharma IQ: When a company is looking to move from a old fashioned, paper based enterprise to a singular information management system, what do they need to consider to prepare themselves for that transition?
Unjulie: Change management in key and bringing people on board.
Having the right sponsors and advocates in the business is really important. Senior stakeholders generally make the decision about buying this software but that message needs to be relayed down to the people who will be using it every day. They need to be given the change to experience and contribute hands on to the configuration and deployment of the software.
April: Really understanding the long-term goal of the organization is key up front. Consider the reporting and insights that are tough to achieve today, and select a system, or tool, that allows you the flexibility to achieve just that. There will be a lot of details and hard work to get to the end result, however; the investment up front will be invaluable over time.
Another approach is to implement solutions with a phased approach. By introducing it to one department at a time, this can prevent a huge impact on the existing business. As the first laboratory gets used to the system and gets on board with the change, when they start gaining efficiencies, the message propagates within the organization creating better user adoption.
Unjulie: Following on from that, another thing to think about is other processes that run in a lab, for example reviewing experiments or sharing information to a customer. It’s important to think about how what you are doing now and the processes you use will change when you implement a solution that is electronic.
April: Absolutely – I have seen a lab who directly moved their paper process to an electronic version of the same form. But where’s the additional value? Instead, as you make this move, you need to start thinking about the data differently when it is electronic. It is a huge paradigm shift, you’re still capturing the same reagent names and equipment IDs, but now as you search on the data entry, you will learn more about your data than you ever could.