Connecting Data to the Patient: Paul McKenzie on J&J Pharma R&D's Lab-to-Patient Program

Aug. 11, 2010
Paul McKenzie, global head of pharmaceutical development and manufacturing sciences for J&J pharmaceutical research and development, shares goals for J&J's pharmaceutical R&D, and the "Lab to Patient" program.

To listen to a longer audio interview, click here, and for an extensive print interview, check September’s issue.

PhM: You’ve moved from a small-molecule to a biotech focus. What have been the biggest challenges involved in making that switch?

PM: In the biotech side of business, two things are increasing in diversity and complexity. One is the pipeline of products that development scientists like myself support, both in development coming out of discovery, and in manufacturing. 

You have a diversity of compounds, MABs through mimetobodies, which are modified monoclonal antibodies, through novel, now smaller molecule protein scaffolds, to stem cells. So you have that pipeline diversity, in addition to diversity of delivery method. 

In biologics, and particularly at Centocor, you move very quickly from the traditional delivery of monoclonal antibodies, in vials, which was typically lyophilized powder, to liquid in vials. But even for liquid in vials, customer expectations have increased to the point where they want the convenience of, say, an auto injector for delivery.

As we move out to more novel protein scaffolds, there may even be demand for additional drug delivery devices such as inhalers or nasal-to-brain-barrier transport systems that are needed. 

So, the amazing thing to me, on the biotech side, is the diversity of the pipeline and the diversity of the delivery methods. This creates the perfect storm in which development scientists can focus their work, to integrate those diversities within those technology areas and ensure that they’re delivering the right product with the right delivery system to the customer.

PhM: Which therapeutic areas is J&J Pharma’s R&D division focusing on now?

PM: Centocor had traditionally focused on immunology, but recently it has expanded its collaboration across J&J Pharma, which has adopted an end-to-end strategy across five main therapeutic areas. At Centocor, which was predominantly the flagship of immunology, we have found that many of the development and discovery groups can now support multiple therapeutic areasnot only immunology, which was the flagship with Remicade, but oncology, neuroscience, infectious disease and cardiovascular metabolism. So biologic targets and biologic potential products are being used across all the therapeutic areas, and that is supported by the Centocor hub. 

PhM: At BMS, you had implemented the “lab-to-plant” strategy. At Centocor, that seems to be extended with lab-to-patient. That name brings the end user of medicines to the forefront, at a time when most industry managers are focused on other key stakeholders. What was the thinking behind the whole lab-to-patient concept and what are your plans for it in the future?

PM: At Centocor, the real lab-to-patient focus was to manage pipeline diversity. Different pipelines, from MAb’s to stem cells, have different patient populations and each of those populations has different expectations for delivery technology. 

As the industry matures and we start to incorporate more advanced clinical diagnostic techniques like biomarkers, where we can start to predict how individual patient populations are responding to a potential new molecular entity, we really need to understand from the patient, back, not only the convenience factor of a device versus a liquid in vial, for instance, or a lyo in vial, but also how an individual patient responds within a given patient population. 

Centocor has looked at how it can invest, on the clinical and discovery side of the business, in understanding biomarkers. That understanding can then influence how a development group examines pipeline and delivery system diversity, as they relate to the patient.

Patients’ expectations, their knowledge of what medicines are available, and their interest in convenience of delivery have increased significantly. This is no different from what has happened in other industries. Today, for example, I have a computer on my belt, where in graduate school, the computer I used required little punch cards. As a result, my expectations of a computer are exponentially different from what they used to be. 

Today, patients want products that are best fit for their purpose. Coming along to help ensure that fit are new clinical diagnostics, diverse pipelines and delivery approaches. 

Thus, it’s critical for any pharmaceutical development group to think backwards from the patient, and how they’re going to achieve all those goals. 

PhM: Has the company changed the way it evaluates the voice of the customer, what the end user needs?

PM: We have reorganized in Johnson & Johnson (J&JPRD) to a therapeutic area, end-to-end focus. This way, therapeutic areas like oncology or immunology can really concentrate on everything from drug discovery targets on out to commercial applications under one accountability within each therapeutic area. 

One of the major things we did, which influences my group in particular, is to institutionalize a process called the Target Product Profile.  This method asks: What is the product? What does it look like, what do we want it to look like at launch, what do we want have it look like at different stages in its lifecycle?

What this approach does is to align the commercial side, sales and marketing, with development and manufacturing, creating a triangle that is aligned much earlier based on what the product is going to be about, what the clinical and lifecycle management goals are, and what commercial goals are, from a lifecycle management view.

PhM: Do you have any specific milestones in mind for the Lab-to-Patient program?

PM: Most goals concern large molecule biologics, small molecule chemistry and formulation devices, and institutionalizing an end-to-end platform from discovery on out to commercial, to ensure that we’re developing and transferring processes in a way that our manufacturing colleagues can readily adopt them. 

Our near-term milestones are really, to institutionalize this concept of platform and to build it into our execution model through the tools that a scientist would use. We’re building out a system-independent recipe model, utilizing the industry standards S-88 and S-95, that can really be layered across any type of technology tool that you choose to implement; electronic lab notebook, or MES. 

Without a system-independent recipe structure, you can end up customizing per product, or per technology, which doesn’t give you the flexibility to evolve quickly as technology products evolve. So we’re working very hard to generate this recipe structure and to ensure that we can utilize it quickly in a multitude of possible technology offerings that come about in any of the spaces, MES or ELN. 

This approach will help us be able to change more rapidly and also, to speak a common vocabulary across development into manufacturing. 

The other thing we’re focusing a significant amount of time on is understanding and developing device platforms. Devices have always been thought about at the later stages, more at the lifecycle management stages versus incorporating them early on in your Target Product Profile, and even incorporating them very proactively in your clinical trials. 

And to do that takes a separate mindset; it takes developing standards that can be utilized in devices across multiple therapeutic areas versus always custom tailoring a device for a given indication. So we’re working very hard with out commercial colleagues to create a library of potential devices that can be leveraged uniformly across multiple therapeutic areas. 

PhM: Can you give an example?

PM: One device that we’ve had some recent success with is our auto injector for Simponi. It was developed internally. That auto injector, we feel, gives the patient, typically, a rheumatoid arthritis patient, more convenience. Now we’re asking whether we can use this platform for other therapeutic areas. We’ve invested in developing it, we’ve invested in making it commercially available, it’s getting good feedback from the commercial market, so how do we take the auto-injector beyond Simponi?

PhM: You mentioned S-88 and S-95. For years, you’ve been saying that S-88 really should become the pharmaceutical industry’s DNA. Are there ongoing cultural challenges getting R&D scientists to become comfortable with this approach? 

PM:  We’re making a significant amount of headway and I think what’s needed is taking the time to work with people and educate them on what S-88 is really about.  For years, S-88 was only viewed as something that the manufacturing group should worry about.  It was how to operate a plant; it was how to write process code. 

What I’ve tried to work on with the teams, and I think they now understand very well, is the importance of collecting information early on, in the way your customer uses information, and operates. 

For instance, one of my major customers is manufacturing. Taking this approach allows two things to happen: first, it allows manufacturing to contribute earlier, because they understand the vocabulary we’re working in, which is very important to me, and, second, it allows us to now compare data across the whole lifecycle of the product. 

Using this approach, you build a regulatory filing as you go, and can show very quickly the history of development, process development or analytical method development by showing changes in the process recipe over time. This, in turn, allows you to compare more quickly a batch made in early developmental stages to one made commercially, also showing process differences and associated analytical differences.

Without that consistent vocabulary and without the adoption of the appropriate technology, it is very difficult to make these comparisons.  That information has historically been stored in static documents, rather than dynamically. 

Being able to compare temperature, pressures, and concentrations across the whole lifecycle of a process development in real time, with graphs and charts and using modeling, is really what I believe the S-88 adoption will do for us. 

PhM: How do you get buy-in from the scientists?

PM: We bring out subject matter experts and show them that what we’re asking them to do, in some regards, is no different from what they already do. Consider a good synthetic chemist, for instance. His or her lab notebook shows that they already tend to think in S-88 termsyou know, bill of materials, equipment classes, procedural stepsthey just don’t realize they’re doing it. 

For them, the challenge is agreeing on recipe components. However, the concept of executing as a recipe really isn’t that far off from what they’re doing day-to-day. It’s just building that library and making sure people work from that common library. 

So it’s really, incredibly important to get subject matter experts in that area adopting. 

We’ve been working very hard to introduce recipe in the analytical area, and you could send process people like myself into an analytical area and preach about S-88 all day long. What really makes the light bulb go off is when you take two or three analytical guys and say, “Alright, work with me a little bit more on that. Let me understand it.”

Once subject matter experts understand the overall goals, they can go in and explain them to their colleagues, in a language they’ll understand. They really get it, as in “So, sample preparation really is a significant series of unit operations that, if we do well, can help the tech transfer and help compare data across the continuum.”

So it’s really just a matter of spending the time. 

I think most people really want to make technology transfer successful. As an R&D scientist, there is no better feeling than seeing your product or your method executed in a plant or in a QC lab. And the more products we can transfer more seamlessly, the easier it is to manage our product over its lifecycle, and the more products we get to work on as R&D scientists. And that is really where you sell the whole concept.

PhM: To what degree have you been able to connect clinical, CAPA and adverse patient response data to sales forecasting, manufacturing and R&D? What approaches and technologies will be needed to make this connectivity between the end user, and operational siloes, possible within the industry?

PM: That connectivity poses a ripe opportunity for us. We still have pillars of say, clinical data, CAPA data, sales and forecasting data.  Where we’re trying to concentrate on right now is to start to understand the questions of interest across those pillars. 

For instance, let’s say I would like to know where the process that I made with Process Approach A was used clinically. I’d have to have my process database that has A, B, C, and D in it and then we have a clinical database of how the trials were run. How do we get a common vocabulary across those so that I can quickly query them to get that question of interest satisfied?

At this point, we’re generating these questions of interest.  Based on those questions, we can then set up a standard data structure which would allow us to extract data quickly. 

This work is at a very early stage, but we’re starting to optimize by asking the cross-pillar questions so that once we get a good cross-pillar solution the pillars, themselves, should be more ready to allow those cross-pillar questions of interest to be answered very readily. 

The amount of data that is generated in all those areas is huge. To mine it well requires standing back and asking, “What kind of mining would we want to do?”

J&J's Pharma R&D’s Informatics group is really taking this on to make sure we can take a more holistic look at this cross-pillar integration of information so that we can make better and more rapid data mining activities a reality, as well as make decisions quicker from that data mining. 

PhM: Yes. Do you see FDA encouraging this type of work or is it still too soon?

PM: Oh, I absolutely think the FDA and other agencies expect us to go move in this direction. A lot of their ability to approve drugs relies on our understanding the interaction between the process, the analytical and the clinical. The better we understand that interaction, and the better the tools are that we put in place to show that interaction very quickly, the more the entire industry would benefit.