Episode 52

Core conflict of pharma labs

Category :  High Variability Operations

Discover how overlooked factors in the generic pharmaceutical sector are leading to significant FDA compliance challenges, and why it's more than just 'analyst error'.

Shubham Agarwal : Welcome to the Counterpoint Podcast. I’m Shubham Agarwal, and we’re going to talk about a very specific aspect of the pharma supply chain today: the pharma quality test labs. Now, these are extremely critical entities of the pharma supply chain. Think of it as inspection gates that every formulation has to go through before entering the market, and there are authorities such as the US FDA and other authorities, which take a very detailed approach, a very critical approach to make sure that everything is pitch perfect, be it the testing equipment, be it the processes, be the people.

They look at everything very, very intricately and make sure that there are no non-conformances because these are drugs which are going to solve people’s problems, which are going to, you know, treat people. So it has to be the best once it goes to the market. In fact, I recently came across an article which states that the USF DA has become extremely critical, especially after COVID, to provide these conformances to the drug manufacturing companies, which makes it even more critical for these pharma companies to implement Good Manufacturing Practices, or as we call it GMPs, to make sure that they conform to the audits of these authorities.

In the same regard, keeping this topic in mind, we have Achal Saran Pande with us, who is a Partner at the Vector Consulting Group and has helped a number of pharma companies to solve the problem of non-conformances, increase their productivity and have them release more drugs faster. Right.

Hi Achal, welcome to the Counterpoint Podcast. How are you?

Achal Saran Pande : I’m fine, Shubham, and thanks a lot for giving me this opportunity to speak on this. This is a very critical issue because regulatory authorities like the US FDA have become very critical, and of late, they have been handing over a lot of non-conformance (NCs) to pharma companies. So this phase is very critical for the pharma industry, and even the Indian government is now looking at manufacturing, pharma manufacturing critically. So it is imperative for the pharma companies to look at their processes, manufacturing processes, their lab processes and restructure, reorganize, so that while implementing these Good Manufacturing Practices, they don’t compromise on productivity, cost and things like that. So yes, it’s a critical issue.
Shubham Agarwal : And is it true that you know, like we said, the US FDA has become extremely critical, especially after COVID? Is that also true?
Achal Saran Pande : Yeah, that’s true. And you would be reading the newspapers as well, the way they are going about it. And that is why it is very important for Indian pharma companies because a lot of these drugs, which are generally drugs, they are exported to the US and other countries. So, for the supplies to sustain, it is very important for companies to look into this. Otherwise, what happens is that if the regulatory authority gives an NC, then it can have negative implications in terms of the supplies can be stopped and whatever future drugs that you are going to want to roll out. Right. They may not allow you to do that. Right. So it has a negative impact on the business.
Shubham Agarwal : So Achal, let’s go deep into the topic and understand why all this happens. But before that, could you explain how does this process work? How does a process inside a pharma testing laboratory work?
Achal Saran Pande : The pharma labs, the testing labs are essentially like small manufacturing plants. And so what, I normally refer to them as pharma testing plants rather than a pharma testing lab.
Shubham Agarwal : Interesting. Okay.
Achal Saran Pande : And why I say this because if you look at the pharma lab, you would find that the material, which in this case is a sample, moves across various specialized workstations, and these workstations perform a very specialized task. So, if I have to describe a lab, a lab can be classified into areas and workstations.
Shubham Agarwal : So, can you tell us, for example, where how all, what all processes are involved? You know, how does the how does the flow work in such labs?
Achal Saran Pande : Yeah, that’s what I’m coming to.
Shubham Agarwal : Yeah.
Achal Saran Pande : So, if I have to look at the areas, which are the broad segments or broad areas in the lab, you will find there is a sample storage area, a sample preparation area, and an instrumentation area. So likewise, there are many broad areas in the lab, and within each area, you’ll find specialized workstations. And so what happens is the sample continuously moves across these workstations within the area and also across areas.
Shubham Agarwal : Okay. So, there are areas which have a specific set of tests. Yes. And there are different areas within the lab? Yes. The sample goes through all the areas as well as through the steps within the area. Yes. Yes. All right.
Achal Saran Pande : And the movement of the sample is essentially carried out by the analyst. So, the analyst is performing the test as well as carrying these samples to different workstations and different areas of the lab.
Shubham Agarwal : Oh, so when you compare it with the manufacturing plant, you know, in a typical manufacturing plant, the product or whatever is being manufactured is moving through work centres, and it’s being handed over and there are people at each work centre who are doing it. But are you saying that it is different in the sense that the analyst is taking the sample across these areas and moving along with the sample? Is that right?
Achal Saran Pande : Yeah. So, as I said, the analyst broadly does two types of activities in the lab. The first activity is his sample preparation and testing. Okay. The second is sample transfer. Right. So what essentially happens is that since the sample moves across workstations, this movement is caused by the analyst. So he’s continuously moving across the lab. Now, you will be surprised to know that, on average, to perform a test, the analyst moves about 4 to 5 kilometres to perform one test in the lab.
Shubham Agarwal : Good for his health, I think.
Achal Saran Pande : Yeah. I do not know whether it’s good for his health or leads to his fatigue. Because, in certain cases, it can lead to fatigue also. But yes, he does that. And what happens is that if the analyst keeps moving, say, 4 to 5 kilometres, then a lot of his time is getting wasted. I’m sure. Right. You know, he’s doing a non-value added kind of an activity.
Shubham Agarwal : Good for his health, I think.
Achal Saran Pande : Yeah. I do not know whether it’s good for his health or leads to his fatigue. Because, in certain cases, it can lead to fatigue also. But yes, he does that. And what happens is that if the analyst keeps moving, say, 4 to 5 kilometres, then a lot of his time is getting wasted. I’m sure. Right. You know, he’s doing a non-value added kind of an activity.
Achal Saran Pande : Right. I’ll give you a very short story. Sure. I was doing a Gemba, which means I was observing an analyst. I was shadowing that analyst in one of the companies. And this analyst was continuously looking at his watch in between. Right. So I was just curious to know why is he looking at the watch at a certain frequency he was doing it. So when I approached, I asked him “Why are you looking at your watch?” So he said, “This is not a watch. This is a fitness meter. So I’m checking how many steps have happened because these are target which I have to complete”. Right. So this is the kind of thing which is happening over there. And I normally refer to this kind of movement of an analyst to the, it is like a Brownian motion of an analyst; if you remember, in science, we used to have a Brownian motion of a particle.
Shubham Agarwal : The particles in a fluid.
Achal Saran Pande : Particles in a fluid. Right. So, the Brownian motion of an analyst which is happening. So, if you consider this pen as an analyst, right, he’s moving zig zag all over the place. He’s moving; if you put a camera, you’ll find he’s moving like this. So it’s like a Brownian motion, which is happening.
Shubham Agarwal : Interesting. But that would really impact the productivity or the efficiency of the analyst as well, isn’t it?
Achal Saran Pande : Yes. So what happens is typically, on average, you would find that about 20% of the capacity of an analyst gets wasted in this motion only. And however, you would be surprised that the capacity loss overall or I would say the productivity loss overall, is about 35%.
Shubham Agarwal : How come?
Achal Saran Pande : It is much more than 20%.
Shubham Agarwal : How come if the capacity lost or the, you know, efficiency loss is about 20%, that is resulting into a 35% loss in overall capacity? I don’t get the relationship.
Achal Saran Pande : So what happens is we have to understand two things over here. The first is that if you look at the test, the analyst performs. He has certain waiting times within the test, these waiting times can be classified into two categories.
Shubham Agarwal : Okay. And what are those?
Achal Saran Pande : The first category is what I call as the Natural Waiting Time. Okay. The other I would call as the Queuing Time.
Shubham Agarwal : Okay. Can you explain those terms a little?
Achal Saran Pande : Yes. So, in natural waiting time, what happens is that there is a certain activity, a specialized activity, which has been initiated by the analyst. Right. And there is a cycle time associated with that activity; you can do nothing about it because that much time is going to happen. Right now, for example, I’ll give you an example in the lab. Suppose the analyst starts, initiates the flushing of the HPLC machine; a flushing of the HPLC machine would take about 1-1.5 hours. It depends on whether he’s doing a water flush or whether he’s doing a mobile phase flush, but 1-1.5 hours. Now, that’s the cycle time.
Shubham Agarwal : So that’s the physics of the environment.
Achal Saran Pande : Yeah, that’s the physics of the environment. So when this activity is initiated, then he has nothing to do. Right. And he has to wait. Okay. Essentially, if he starts waiting and if he starts doing all his activities in sequence, his test would expand and would go beyond his working hours. Right. So what he does is, when he does such kind of activities, he initiates many other parallel activities to save his time. So he will put this HPLC machine flushing, he would initiate it and then parallelly start 2-3 activities, he’ll start doing those activities.
Shubham Agarwal : But these are not different samples that you testng he’s concentrating on one sample at a time, right?
Achal Saran Pande : Yes. For a sample, at any point in time, he has initiated this activity. Right. Right. And there are other activities associated with the sample which he would like to do. Okay. What he also does is sometimes, for the previous sample, he starts to document also, if he has left it or some training, which may happen, or he’s called for that training, something or the other activity will also come in. The key is that many parallel activities get started at any point in time. Right. So, this is the Natural Waiting Time I was talking about. Right.
Shubham Agarwal : So, Achal, we were also, you know, you had mentioned the second aspect or the second element, that’s the Queuing Time. So, can you explain that as well?
Achal Saran Pande : Yes. The second waiting time is the queuing time. A queuing time essentially happens in areas where there are shared resources. Okay. So, for example, if I talk about weighing balance, there would be many analysts coming to the same balance to weigh their samples. And so, in the process, the queue formation happens. And so the waiting time gets built in, in the process. Again, what the analyst, the analyst has a choice: if he sees that the queue is too large, it’s going to take a lot of time for him, and a lot of time will get wasted. He will go and initiate some other activity. So, again, in this case, also, because of the queueing time, he initiates multiple activities so that he can save the time he has. And he can perform the test and go back home, perform the tests within his working hours and go back home and does not have to do overtime over there. So that is what he thinks, and that’s why he wants to do and start and do everything very quickly.
Shubham Agarwal : But he cannot skip the process. The process of let’s say the weighing of the sample. He does not skip. He doesn’t have the, you know, the option to skip that process, isn’t it?
Achal Saran Pande : He does not want to do that, essentially. Okay. Yeah. It’s not a conscious decision to skip because it’s a crime.
Shubham Agarwal : But it may happen.
Achal Saran Pande : Yes. So now we have to understand why it happens. See, if I start a lot of activities together, I will have a lot of motion of the analyst, so he will go from here, there, everywhere. So this kind of loss, I’ve already mentioned, is the Motion Loss. The other thing which happens is if I, if you start, if the analyst starts too many activities, then he has a lot of information. I would say information overload, a lot of thinking going on in his mind. Right. We call it as ‘Cognitive Overload’. Right. And when you get too many things going in your mind, there’s bound to be skips and misses. Right. And you would have also observed in your life that you start doing multi-tasking, you tend to make errors. Right. You would have experienced it, correct? This is exactly what happens in terms of the analyst starting too many activities, and we call this as ‘Cognitive Overload’. And so it’s not a conscious decision by the analysts to skip and miss, but it happens because of the way he’s operating over there. So, one loss is a Motion Loss. The other loss is what we call a loss due to Cognitive Overload due to the multi-tasking that he does, starting too many activities together. But you see what happens in the process: if he does a skip, if there’s a skip or a miss, some errors happen. Right. So these errors, these mistakes I would say, they manifest into errors such as what we call as the OOS, OOT incidents – all these are errors which happen in the lab.
Shubham Agarwal : Okay. Can you briefly explain these terms, OOS and OOT, exactly?
Achal Saran Pande : OOS is essentially Out Of Specification. Okay. OOT is Out Of Trend incident. These are different kinds of errors which happen.
Shubham Agarwal : These are jargon within the pharma.
Achal Saran Pande : Yeah, these are normally used in pharma. Right. So when such errors happen, then a lot of CAPAs are taken, and actions are taken.
Shubham Agarwal : The Corrective And Preventive Actions (CAPA).
Achal Saran Pande : Corrective. Yes, Corrective And Preventive Actions (CAPA) are taken so that these errors don’t happen in the future. Right. But you see the CAPA process is a very exhaustive process. Right. And it sucks in a lot of time of the analyst. So, I create an error. Yeah. Then I have to do a CAPA, this CAPA sucks in a lot of capacity of the analyst. And when it sucks in a lot of capacity of the analyst, what happens is essentially it’s a vicious loop.
Shubham Agarwal : Correct.
Achal Saran Pande : It reduces the time of the analyst to do the test, the available time.
Shubham Agarwal : This has to come from the available bandwidth itself.
Achal Saran Pande : Yes. Because, again, he has certain working hours. Right. And when it sucks out the capacity of the analyst, what would the analyst do? He will start more activities, parallelly, causing more errors.
Shubham Agarwal : Or he might skip some steps.
Achal Saran Pande : Skipping would happen as a result.
Shubham Agarwal : Right.
Achal Saran Pande : I would say it’s like a symptom. Yeah. Right. So, the vicious loop is – I create errors, my capacity reduces, which means the time available reduces, so I have only this small amount of time available with me. I have to perform the test, so I will open more activities. I open more activities, I’ll be making more errors. It goes on and on and on. It’s a Vicious Loop. So this is exactly what happens in the lab.
Shubham Agarwal : So Achal, what we’ve established is how the, you know, the losses add up to each other and create a vicious loop in the entire testing laboratory, the testing lab. And, it is very clear that we have helped a lot of companies break this vicious loop, come out of this vicious loop, increase their productivity and reduce some of these errors, some of these losses, like the Motion Error Loss or the Fatigue Loss or the Analyst Error Loss. Can you tell us how do we start this journey of breaking the vicious loop? Where is that first point where we can make an intervention?
Achal Saran Pande : Yeah. So as I discussed the losses, these losses have to be arrested. Okay. That we have been discussing right now. So, which essentially means that what we’ll have to do is we have to restrict the motion of the analyst. We have to ensure that that parallel working that he does, does not happen. Right. And for doing that what we have to do is, we have to take certain activities club them into work groups. And then these activities have to be performed by certain defined resources. Right. And not by the same analyst.
Shubham Agarwal : Okay.
Achal Saran Pande : Right. So when this is done by certain defined resources, who are focusing on those activities only. So, that focus actually helps in reducing the errors or the skips and misses that normally happen. It also reduces fatigue because there’s no motion, and the entire concentration of these resources is on the work group or the set of activities that they’re doing. Right. Essentially, that is the broad solution element that we have to put in place.
Shubham Agarwal : Okay. So, but then would it not increase the amount of resources that are required when you make these work packets, when you make these resource groups, the number of people, the resources that are required or, you know, to do these activities, which was until now being done by a single analyst, do they not increase the number of resources that are required in the system and thereby increase the cost for the organization.
Achal Saran Pande : See, what happens is we have to look at it from the other side. Yeah. And the other side essentially is, remember I just told you that there is almost a 35% productivity loss, which is associated with the analyst.
Shubham Agarwal : Yeah. Right.
Achal Saran Pande : If I have to put it in a very layman’s terms, a 35% productivity loss of an analyst is essentially, if you are recruiting one analyst, you are not recruiting one analyst; you are recruiting 0.65 analysts, right? Now assume that I arrest all the 35% loss that is happening. This is a pure capacity increase, or you can see a regeneration within the lab. Yeah. You invest that capacity back into the system.
Shubham Agarwal : And without increasing any cost.
Achal Saran Pande : Without increasing the cost.
Shubham Agarwal : Because although you have hired a 0.65 analyst, you’re still paying one.
Achal Saran Pande : Yes. Yes. So you reinvest the capacity into the lab and reorganize the lab and restructure the lab. Okay. So you don’t have to restructure in terms of the people, but also in terms of the work groups, the activities associated with that. So then you would find the errors would go down significantly. Right. In fact, I would say it makes the life of the analyst much better. Okay. You see, in the pharma industry, you will see there’s a lot of attrition which happens in the lab. A lot of attrition. It ranges between 15%-40%.
Shubham Agarwal : That’s extremely high.
Achal Saran Pande : The more the analyst does overtime in a company, the more his tendency to leave the company. Right. Because he cannot do overtime continuously. Yeah. They keep switching jobs from time to time.
Shubham Agarwal : And it must be increasing the stress levels of the analyst.
Achal Saran Pande : It increases the stress because the moment the analyst or certain analysts leave the company, temporarily, that workload comes on to the existing analysts who are working, right? Again, it’s a vicious loop. Now, you under is. So it is very important that we make their life better so that they can go home on time. And this is a very big pain point of the analyst if you go to any company. It is one of the major pain points of the analyst. He wants to go back home after certain working hours. He does not want to do overtime very frequently. And this is the softer aspect of it. Yeah. Obviously, in terms of retention, because the moment the analyst leaves, he’s a trained or he or she is a trained analyst.
Shubham Agarwal : And it must be increasing the stress levels of the analyst.
Achal Saran Pande : It increases the stress because the moment the analyst or certain analysts leave the company, temporarily, that workload comes on to the existing analysts who are working, right? Again, it’s a vicious loop. Now, you understand what is the vicious loop is. So it is very important that we make their life better so th So it is very important that we make their life better so that they can go home on time. And this is a very big pain point of the analyst if you go to any company. It is one of the major pain points of the analyst. He wants to go back home after certain working hours. He does not want to do overtime very frequently. And this is the softer aspect of it. Yeah. Obviously, in terms of retention, because the moment the analyst leaves, he’s a trained or he or she is a trained analyst.
Shubham Agarwal : So then you will have to invest in the training.
Achal Saran Pande : You will have to invest in retraining all that. You can avoid that. So that’s also a cost that is coming to the company, which is not very evident. Yeah. But it is there.
Shubham Agarwal : Right. Yeah. So, the actual cost of those errors happening and the stress that is being caused to the analyst is extremely huge. It manifests in very various forms. Yes. Tell me, during the implementation that you’ve done, has the number of non-conformances reduced? How has it helped these companies? If you can briefly mention about this.
Achal Saran Pande : So, the clients with whom we have worked have sailed smoothly through all FDA audits. Okay. And they have been appreciated also. It’s not about sailing through the audits, but in terms of the appreciation and if you get the appreciation from such regulators, and they are very strict. Yeah. If you can just sail through right, then your chances of getting businesses, future businesses, and also growing the existing business increases
Shubham Agarwal : Significantly, I’m sure
Achal Saran Pande : Then you are in their good books.
Shubham Agarwal : Right.
Achal Saran Pande : And that is very important. Right.
Shubham Agarwal : Achal, I also want to highlight one other aspect that I’ve heard, which is called the ‘Method Specific Errors’. Can you tell us what this is and how it manifests in the lab, the testing lab?
Achal Saran Pande : Okay. So, the Method Specific Errors is a big topic by itself. Okay. I would suggest taking in a subsequent podcast because that will require a lot of detailing.
Shubham Agarwal : Right. So we can say that you know, we unveil the first layer of the errors or the challenges that are there in a pharma lab. And this is probably the second layer, which is a deeper layer. And can only be unveiled once the first layer has been solved and dealt with. Yes. Right. Great, Achal; I think that was a great discussion. And we could go deep down into, you know, the specifics of what the problems are and how we start on that journey of solving some of these problems. So, thank you once again for your time and for this great podcast.
Achal Saran Pande : Thanks, Shubham, and I will definitely look forward to the second podcast right, which will be a sequel, I believe, and it will help people to understand what the Methods Specific Errors are and, how to look at it and how to eliminate it. Right? So I will definitely look forward to that.
Shubham Agarwal : So great. Thank you to all the listeners for listening to the podcast and giving us your time. We keep coming back with more episodes and we’ll definitely come back with the subsequent episode on this topic, take a more detailed approach and understand what these Method Specific Errors are. Until then, this is Shubham signing off.

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