WEBVTT X-TIMESTAMP-MAP=LOCAL:00:00:00.000,MPEGTS:0 eb47089c-62c7-4ad4-b267-120945a0c6c9-0 00:00.160 --> 00:03.814 Good afternoon and welcome to Enabling Analytical and QC Labs eb47089c-62c7-4ad4-b267-120945a0c6c9-1 00:03.814 --> 00:04.640 of the Future. 1952b28e-29ad-4086-8e55-99dcb5b0506b-0 00:05.000 --> 00:07.280 This is a sponsored session by Deloitte. d83c5428-e01c-4202-90a2-f92c12a017e6-0 00:07.960 --> 00:11.164 I am Brandon Pastor, I'm Senior Director of professional d83c5428-e01c-4202-90a2-f92c12a017e6-1 00:11.164 --> 00:14.819 development here at ISP E, so if you're interested in all things d83c5428-e01c-4202-90a2-f92c12a017e6-2 00:14.819 --> 00:18.417 training, I'm the person to see for that, whether it's a custom d83c5428-e01c-4202-90a2-f92c12a017e6-3 00:18.417 --> 00:21.622 event or a public event as well, anything under that QMS d83c5428-e01c-4202-90a2-f92c12a017e6-4 00:21.622 --> 00:24.040 umbrella, umbrella we are responsible for. d5ae564c-c2d7-49b2-b1e2-bc162f034386-0 00:24.640 --> 00:27.512 I just want to give you a few bits of information before we d5ae564c-c2d7-49b2-b1e2-bc162f034386-1 00:27.512 --> 00:27.800 start. cd1c58ba-9b6d-4ed8-8679-b7ade9cdb7ac-0 00:28.600 --> 00:32.389 BIOS for these two speakers are available online if you want to cd1c58ba-9b6d-4ed8-8679-b7ade9cdb7ac-1 00:32.389 --> 00:34.640 read further about their backgrounds. a0b5e45d-ec9c-4d29-a049-6cdedc8149e2-0 00:34.920 --> 00:37.800 They're also on the conference website and the app as well. 79e970d4-950f-40a1-be98-57394c483cba-0 00:38.480 --> 00:42.172 If there's any handouts that are associated with this, there's 79e970d4-950f-40a1-be98-57394c483cba-1 00:42.172 --> 00:46.040 they're available electronically on the virtual platform as well. 31a95251-b761-4c4c-843f-9f00c38d4e56-0 00:46.800 --> 00:50.295 These sessions are on demand and their recordings will be on a 31a95251-b761-4c4c-843f-9f00c38d4e56-1 00:50.295 --> 00:53.680 virtual platform about 3 weeks after the conference is over. 19c51c94-107b-4004-8f52-4cc18c905b6f-0 00:53.920 --> 00:56.280 They'll be available for about 90 days as well. b11c8691-e72b-46bb-870f-44761166d7d0-0 00:57.240 --> 00:58.720 This session is being recorded. a40ad1e2-8a5a-4393-bc7a-bde290a53b5d-0 00:58.720 --> 01:01.976 So I ask speakers as well as anyone who has a question, a40ad1e2-8a5a-4393-bc7a-bde290a53b5d-1 01:01.976 --> 01:05.349 speak, wait for the mic and speak clearly into the mic as a40ad1e2-8a5a-4393-bc7a-bde290a53b5d-2 01:05.349 --> 01:05.640 well. 06d74eb7-280f-44a1-96bb-8f7aeafa3257-0 01:06.480 --> 01:08.880 Now allow me to introduce your, your two speakers. 1d5a1812-a2e7-4295-bcef-f588817ad340-0 01:10.120 --> 01:16.045 So immediately to my right as LAX Ernakil and his colleague is 1d5a1812-a2e7-4295-bcef-f588817ad340-1 01:16.045 --> 01:17.080 Iknam Gill. a5169dda-a608-438e-9415-5eec727bb768-0 01:17.720 --> 01:19.240 I'll, I'll allow you to take it away. 219e9c96-1149-4bae-9a00-58397a6f3091-0 01:19.520 --> 01:22.515 And they have asked me to allow you to ask questions while 219e9c96-1149-4bae-9a00-58397a6f3091-1 01:22.515 --> 01:23.480 they're presenting. 6c71e5bf-9368-48b9-b4f0-2673ceea4f63-0 01:23.480 --> 01:26.100 So if you need a question, just get my attention and I'll bring 6c71e5bf-9368-48b9-b4f0-2673ceea4f63-1 01:26.100 --> 01:26.920 the mic over to you. 6e09d289-a477-44e8-a774-766a595f3919-0 01:28.400 --> 01:28.680 Thank you. e46c0efa-f59b-4567-b74f-47ca45fc0922-0 01:30.440 --> 01:31.320 Thank you very much. dd0d43f3-f1f7-4cbb-9d56-fbc0b0bcd066-0 01:31.800 --> 01:33.000 Good afternoon, everybody. a735b586-44f1-431a-82d1-fab3c0f17186-0 01:33.000 --> 01:36.360 And we are the last step between you and drinks. 5a0f71c1-699f-4c0b-97ac-35be51459447-0 01:36.360 --> 01:40.360 So we will try and make this as interesting as we can. a939d3fd-ff7b-413e-a2a0-6a77ec8a4699-0 01:41.240 --> 01:43.940 My name is Loxburn and Kailai lead Deloitte's Life Sciences a939d3fd-ff7b-413e-a2a0-6a77ec8a4699-1 01:43.940 --> 01:44.840 operations practice. 55f9470e-2aa6-4897-b74a-48d093aeddde-0 01:44.840 --> 01:47.897 That's all things from process development, analytical 55f9470e-2aa6-4897-b74a-48d093aeddde-1 01:47.897 --> 01:51.010 development all the way to planning and fulfillment and 55f9470e-2aa6-4897-b74a-48d093aeddde-2 01:51.010 --> 01:52.400 distribution of products. 785ff6bd-599a-42f5-8e61-54639757f50e-0 01:52.400 --> 01:55.891 So all of that operations practice, that's the practice 785ff6bd-599a-42f5-8e61-54639757f50e-1 01:55.891 --> 01:59.320 that I represent what we're going to talk about today. e638dba6-7082-463d-90ff-db6088a38d3f-0 01:59.320 --> 02:04.249 I want to start with an anecdote that I typically use because it e638dba6-7082-463d-90ff-db6088a38d3f-1 02:04.249 --> 02:08.800 comes from my thesis advisor who is a who's a researcher in e638dba6-7082-463d-90ff-db6088a38d3f-2 02:08.800 --> 02:11.000 pharmaceutical manufacturing. 176a2d4e-d7f8-4ff4-acea-7ce86025a9af-0 02:11.000 --> 02:14.872 And one of the things that he would say always is the best way 176a2d4e-d7f8-4ff4-acea-7ce86025a9af-1 02:14.872 --> 02:18.560 to reduce variability in your process is to not measure it. 17483a9c-49b6-4ccf-9574-5ddc945659dd-0 02:20.880 --> 02:21.440 Looking for. e6b5afc9-a670-4e85-9f32-0fa9dffd7124-0 02:21.440 --> 02:22.440 OK, there you go. de0bdcb0-e9d3-4252-aa15-c68c1262bcfa-0 02:22.640 --> 02:26.311 So then the next best way obviously is to measure it when de0bdcb0-e9d3-4252-aa15-c68c1262bcfa-1 02:26.311 --> 02:29.160 the process is happening on the line, right? 2c6456de-1721-42f5-92f7-9e2c4c9002ea-0 02:29.160 --> 02:31.788 So that's what process analytical technologies is all 2c6456de-1721-42f5-92f7-9e2c4c9002ea-1 02:31.788 --> 02:32.080 about. 397301a0-b56f-4d44-a896-788ffa37f848-0 02:32.480 --> 02:36.687 But the end to end process analytical technology revolution 397301a0-b56f-4d44-a896-788ffa37f848-1 02:36.687 --> 02:38.160 is still in underway. a222ac54-b19d-46da-af06-7b9c2eaf51a1-0 02:38.240 --> 02:41.925 And with the limits of physics and with the limits of chemistry a222ac54-b19d-46da-af06-7b9c2eaf51a1-1 02:41.925 --> 02:45.438 and with the limits of other challenges that we have, we may a222ac54-b19d-46da-af06-7b9c2eaf51a1-2 02:45.438 --> 02:46.360 never get there. 6b64fe20-48b4-4b2d-86f1-c493e8affd86-0 02:46.360 --> 02:47.960 We could, but we may never get there. 2643d38b-a5b9-4bd7-81cb-71e7abc129c4-0 02:48.600 --> 02:52.954 So then what remains to be seen is how can we use what we've 2643d38b-a5b9-4bd7-81cb-71e7abc129c4-1 02:52.954 --> 02:57.450 been dealt with, the cards we've been dealt with, which is the 2643d38b-a5b9-4bd7-81cb-71e7abc129c4-2 02:57.450 --> 03:00.520 labs of today, to take them to the future. 2610f116-730b-4208-b6ec-21f60b85f6c7-0 03:00.720 --> 03:02.040 And that's what this talk is about. 010dc7f9-4f81-4cd0-a86d-75e7819bc4e4-0 03:03.600 --> 03:03.840 What? 3a8ff842-059d-4085-a9c7-5cb070200ed6-0 03:03.840 --> 03:04.680 What we'll start. fd724e83-94da-48f3-8829-e6e89cba8314-0 03:04.880 --> 03:07.520 We have 3 acts if you go to the next page. 66281ac3-68b4-4e06-8e4d-a5d0241cbbb6-0 03:07.600 --> 03:11.080 I'm used to saying if you go to the next page. 49f67775-fc21-4136-b8db-047624a6bb0c-0 03:11.080 --> 03:12.920 So we have 3 acts. bcd9023b-e804-46bd-9956-2ca5aae99f6b-0 03:12.920 --> 03:16.411 We are going to start first by painting a picture of the bcd9023b-e804-46bd-9956-2ca5aae99f6b-1 03:16.411 --> 03:16.840 future. 10367128-dc64-4732-b067-bfd335fec8fa-0 03:18.200 --> 03:20.702 Another anecdote that my advisor would say always is if you don't 10367128-dc64-4732-b067-bfd335fec8fa-1 03:20.702 --> 03:22.560 know where you're going, you'll never get there. 7333af91-66a2-4102-8a6a-c8c9f6f35b25-0 03:22.560 --> 03:26.528 So we want to be able to paint the picture of what is it, how 7333af91-66a2-4102-8a6a-c8c9f6f35b25-1 03:26.528 --> 03:30.240 can we imagine with the cards we've been dealt with for a 7333af91-66a2-4102-8a6a-c8c9f6f35b25-2 03:30.240 --> 03:31.200 future in labs? 517686ca-41f6-4f88-a27c-81a2aa44963c-0 03:32.120 --> 03:35.809 The second act is in order to get there, you need to build an 517686ca-41f6-4f88-a27c-81a2aa44963c-1 03:35.809 --> 03:37.000 appropriate vehicle. f14d7df5-6f90-4e78-b404-c6b94cd053a8-0 03:37.320 --> 03:41.289 You need to build some form of an enabler or enablers to get f14d7df5-6f90-4e78-b404-c6b94cd053a8-1 03:41.289 --> 03:41.680 there. bcee0845-9dbe-46c6-b3b3-e122b4d0cffd-0 03:41.680 --> 03:45.516 So we'll talk about what those enablers are and how those bcee0845-9dbe-46c6-b3b3-e122b4d0cffd-1 03:45.516 --> 03:49.617 enablers will get you along the tracks or the OR the paths to bcee0845-9dbe-46c6-b3b3-e122b4d0cffd-2 03:49.617 --> 03:50.080 change. dd843307-7310-4d4a-9d70-5f13e5a7e66a-0 03:50.520 --> 03:52.600 And then we'll talk about that journey. c1982c72-0038-4e9d-878c-cb1fa57251f6-0 03:53.160 --> 03:55.480 All of you all will not be on the same journey. 2c11d342-15ae-4ef1-b1d8-a585416f6df4-0 03:55.480 --> 03:59.535 So what does that look like for different people or different 2c11d342-15ae-4ef1-b1d8-a585416f6df4-1 03:59.535 --> 04:03.590 different entities, different companies coming from different 2c11d342-15ae-4ef1-b1d8-a585416f6df4-2 04:03.590 --> 04:05.880 walks of life, different legacies. cc6ebc55-b9cb-4613-ac58-6f9c62633413-0 04:05.880 --> 04:09.021 So we'll talk about those three acts and I'll start with the cc6ebc55-b9cb-4613-ac58-6f9c62633413-1 04:09.021 --> 04:12.266 first one and then my colleague will continue the conversation cc6ebc55-b9cb-4613-ac58-6f9c62633413-2 04:12.266 --> 04:13.760 around talking about enabler. 6f5c1671-e257-4348-a5f3-6f574c5de63b-0 04:13.760 --> 04:17.844 So first and foremost, let's let's all imagine kind of you 6f5c1671-e257-4348-a5f3-6f574c5de63b-1 04:17.844 --> 04:20.960 know where what the future should look like. 8b883359-8e5b-44f6-ba15-a8eedb9c07a0-0 04:20.960 --> 04:24.201 But before we do that, it's important to understand the why, 8b883359-8e5b-44f6-ba15-a8eedb9c07a0-1 04:24.201 --> 04:24.520 right? 46565e47-f669-427c-8c21-5e1688294fb5-0 04:24.520 --> 04:28.824 I think we all agree that today's pharmaceutical and 46565e47-f669-427c-8c21-5e1688294fb5-1 04:28.824 --> 04:32.560 biopharma in mind meant is much more complex. e0e74ff5-c8e0-4561-ac17-df5dc1b71312-0 04:32.560 --> 04:34.520 I think every one of us live this every day. 66bd9d07-782e-4a44-8911-4e5f5c614079-0 04:35.200 --> 04:39.117 There are products that have unique requirements in terms of 66bd9d07-782e-4a44-8911-4e5f5c614079-1 04:39.117 --> 04:42.778 whether it's product quality attributes, whether it's in 66bd9d07-782e-4a44-8911-4e5f5c614079-2 04:42.778 --> 04:46.568 process controls, material controls, the number of complex 66bd9d07-782e-4a44-8911-4e5f5c614079-3 04:46.568 --> 04:50.486 tests and evaluations that you have to do for a product have 66bd9d07-782e-4a44-8911-4e5f5c614079-4 04:50.486 --> 04:54.211 significantly explored that correspondingly has increased 66bd9d07-782e-4a44-8911-4e5f5c614079-5 04:54.211 --> 04:57.680 the number of tests that you're running in your labs. bd6421be-a63a-4da8-9698-f0296ed8c04e-0 04:58.760 --> 05:01.727 Your, your regulators are looking at it and saying you bd6421be-a63a-4da8-9698-f0296ed8c04e-1 05:01.727 --> 05:04.856 need to, you need to get repeatable tests and you need to bd6421be-a63a-4da8-9698-f0296ed8c04e-2 05:04.856 --> 05:08.417 assure that your quality is what it is and, and, and that you can bd6421be-a63a-4da8-9698-f0296ed8c04e-3 05:08.417 --> 05:10.360 actually stand behind your results. 85f92a51-f6f0-4e5b-a677-f5fe2c05f80a-0 05:10.360 --> 05:12.937 And then of course, on the other side, your colleagues in 85f92a51-f6f0-4e5b-a677-f5fe2c05f80a-1 05:12.937 --> 05:15.382 commercial are saying, hey, when can I get the product 85f92a51-f6f0-4e5b-a677-f5fe2c05f80a-2 05:15.382 --> 05:17.915 immediately in the market because I don't want to lose a 85f92a51-f6f0-4e5b-a677-f5fe2c05f80a-3 05:17.915 --> 05:20.493 days of sale in the market and impact patients on a daily 85f92a51-f6f0-4e5b-a677-f5fe2c05f80a-4 05:20.493 --> 05:20.760 basis. c0fd94ac-b4f2-4282-9066-9ff89debe28a-0 05:20.760 --> 05:24.720 Not only that, there was another conversation we had this morning c0fd94ac-b4f2-4282-9066-9ff89debe28a-1 05:24.720 --> 05:28.620 where you don't have the people to be able to do all these tests c0fd94ac-b4f2-4282-9066-9ff89debe28a-2 05:28.620 --> 05:30.720 available in the market right now. f0c293b8-dba9-4d03-894c-14b8b683a8fa-0 05:30.720 --> 05:34.194 Like there is a significant war on talent, there's a gap in f0c293b8-dba9-4d03-894c-14b8b683a8fa-1 05:34.194 --> 05:34.600 talent. d6bc931d-3937-4714-a714-e8c067fa2950-0 05:34.600 --> 05:38.821 So how do you get to a future state that you can actually d6bc931d-3937-4714-a714-e8c067fa2950-1 05:38.821 --> 05:42.680 satisfy many of these challenges that you're facing? 843a8bb9-6179-4d06-93dc-2d077963c597-0 05:43.400 --> 05:47.684 And So what we see when we think about a lab of the future, we 843a8bb9-6179-4d06-93dc-2d077963c597-1 05:47.684 --> 05:51.969 imagine a lab where biometrics will define which operators are 843a8bb9-6179-4d06-93dc-2d077963c597-2 05:51.969 --> 05:55.506 coming to the floor and when they and when they are 843a8bb9-6179-4d06-93dc-2d077963c597-3 05:55.506 --> 05:59.587 performing their tests on the floor, they get trained, they 843a8bb9-6179-4d06-93dc-2d077963c597-4 05:59.587 --> 06:03.600 have the capabilities to execute the tests live and ready. a8f82d05-cfd1-4f1a-8506-1b7e118815f9-0 06:04.440 --> 06:08.983 The samples are being prepared in an automated way and are a8f82d05-cfd1-4f1a-8506-1b7e118815f9-1 06:08.983 --> 06:13.295 getting to the sampling stations, to the to the testing a8f82d05-cfd1-4f1a-8506-1b7e118815f9-2 06:13.295 --> 06:15.760 stations in an automated manner. f9f9e443-33f2-4e2e-8eea-31bc227bdf20-0 06:16.280 --> 06:20.436 The outcomes are captured digitally and automatically kind f9f9e443-33f2-4e2e-8eea-31bc227bdf20-1 06:20.436 --> 06:24.592 of trans transferred over to where the the the analysis is f9f9e443-33f2-4e2e-8eea-31bc227bdf20-2 06:24.592 --> 06:25.720 being performed. ca262b57-9cf9-48c3-ad9c-df610570425e-0 06:26.360 --> 06:30.040 Exceptions are automatically classified. 0c159c8d-7afd-47d2-a5ee-3922bb06c727-0 06:30.040 --> 06:33.935 Exceptions are automatically kind of defined and, and managed 0c159c8d-7afd-47d2-a5ee-3922bb06c727-1 06:33.935 --> 06:37.705 and, and, and delivered to the right authorities within the 0c159c8d-7afd-47d2-a5ee-3922bb06c727-2 06:37.705 --> 06:41.788 within within the company to to adjudicate what to do with those 0c159c8d-7afd-47d2-a5ee-3922bb06c727-3 06:41.788 --> 06:42.480 exceptions. 9023d247-ce4b-4b3d-9c1e-4b4baf3cc5f2-0 06:42.800 --> 06:45.726 And then of course the end to end process is continuously 9023d247-ce4b-4b3d-9c1e-4b4baf3cc5f2-1 06:45.726 --> 06:47.240 monitored on an ongoing basis. 825db587-821b-4a53-83db-fcd409829cac-0 06:47.480 --> 06:50.115 So you can actually get more from the assets that you've 825db587-821b-4a53-83db-fcd409829cac-1 06:50.115 --> 06:51.040 already invested in. 0577063a-0d93-40b7-8925-3a9e733fe9b9-0 06:51.520 --> 06:57.200 So this is an achievable outcome. b93cbc8a-aa5a-4853-8060-1fc4800048e0-0 06:57.200 --> 07:00.832 And this is achievable because as my colleague will shortly b93cbc8a-aa5a-4853-8060-1fc4800048e0-1 07:00.832 --> 07:04.100 talk about it, some of the features that belie how to b93cbc8a-aa5a-4853-8060-1fc4800048e0-2 07:04.100 --> 07:06.280 achieve this future is all present. 9143c60e-90f8-49ca-947d-22192d0e0e36-0 07:06.320 --> 07:09.576 And so with that, I will pass it to Iknam who will talk about 9143c60e-90f8-49ca-947d-22192d0e0e36-1 07:09.576 --> 07:12.360 kind of what what it is to kind of get get us there. 4a48d536-59b9-45fe-a768-cc9211ebaae3-0 07:12.360 --> 07:19.158 Before I jump in any questions, please feel free to to stop us 4a48d536-59b9-45fe-a768-cc9211ebaae3-1 07:19.158 --> 07:24.770 at any time if you wanted to during the discussion. 0ba6c3e9-8cb2-410b-adfe-cf1190edff82-0 07:24.770 --> 07:30.021 So building on what Lux just said, if I can get to the next 0ba6c3e9-8cb2-410b-adfe-cf1190edff82-1 07:30.021 --> 07:35.273 line, if I can use this right, yeah, a lot of what we think 0ba6c3e9-8cb2-410b-adfe-cf1190edff82-2 07:35.273 --> 07:40.612 about as key enablers for what can be a lab of the future or 0ba6c3e9-8cb2-410b-adfe-cf1190edff82-3 07:40.612 --> 07:41.399 next Gen. a26f8128-4581-4f20-b258-5454cbc9ea84-0 07:41.400 --> 07:45.761 labs are very well, I would say distributed in either the a26f8128-4581-4f20-b258-5454cbc9ea84-1 07:45.761 --> 07:48.920 manufacturing space or the R&D space. 28b5e414-8378-4953-8165-c3256d3b72a5-0 07:48.920 --> 07:52.744 And essentially what what we're looking at from a lab 28b5e414-8378-4953-8165-c3256d3b72a5-1 07:52.744 --> 07:56.497 perspective is for QC and analytical laboratories to 28b5e414-8378-4953-8165-c3256d3b72a5-2 07:56.497 --> 08:00.887 essentially I, I want to say catch up, but I also want to say 28b5e414-8378-4953-8165-c3256d3b72a5-3 08:00.887 --> 08:02.799 that become the focus area. 1d40703a-1a97-47eb-b3f8-d9c4a4c3d38f-0 08:03.720 --> 08:08.275 Analytical typically has been an afterthought and we focus on 1d40703a-1a97-47eb-b3f8-d9c4a4c3d38f-1 08:08.275 --> 08:12.830 manufacturing and we focus on manufacturing quality, capacity 1d40703a-1a97-47eb-b3f8-d9c4a4c3d38f-2 08:12.830 --> 08:14.080 and optimization. 3800d5e4-acfe-4302-b9e6-330be5bdc1c6-0 08:14.560 --> 08:19.000 But we're starting to see labs quickly become the bottleneck. d8d6a516-b6ca-4aa3-bc60-e2906c18f9db-0 08:19.520 --> 08:22.785 And So what you see here, the trends that are catalyzing d8d6a516-b6ca-4aa3-bc60-e2906c18f9db-1 08:22.785 --> 08:26.280 change may already be very well understood from other areas. 9a2d5852-2cc6-48e1-b0c0-66b0310ae3a2-0 08:26.280 --> 08:29.460 But what I want to talk about is really how are they coming into 9a2d5852-2cc6-48e1-b0c0-66b0310ae3a2-1 08:29.460 --> 08:30.880 play in the laboratory space. 4582129f-c5da-4212-8ca6-6fb3edc05e02-0 08:31.280 --> 08:35.808 So digital operations is probably where we have the most 4582129f-c5da-4212-8ca6-6fb3edc05e02-1 08:35.808 --> 08:40.655 kind of effort and progress today with not just foundational 4582129f-c5da-4212-8ca6-6fb3edc05e02-2 08:40.655 --> 08:45.581 software like limbs etcetera, but also workflow optimization, 4582129f-c5da-4212-8ca6-6fb3edc05e02-3 08:45.581 --> 08:48.760 some some level of automation etcetera. 86920b03-73cc-406a-81a1-f25a05db7090-0 08:48.760 --> 08:52.133 So really kind of building what is a lot of work being done in 86920b03-73cc-406a-81a1-f25a05db7090-1 08:52.133 --> 08:55.560 the digital operations within a laboratory within a QC setting. ab4778d2-f999-4857-a9b5-f8eca2f115e4-0 08:55.600 --> 09:00.875 For example, assets has taken, I would say a big turn with the ab4778d2-f999-4857-a9b5-f8eca2f115e4-1 09:00.875 --> 09:06.235 advances in PAT, not just from when PAT started, but to, to the ab4778d2-f999-4857-a9b5-f8eca2f115e4-2 09:06.235 --> 09:09.920 frequency in which PAT is being used today. 8bf2e754-2af1-4040-8bc5-68370794e367-0 09:09.920 --> 09:13.504 And then how those assets are essentially integrated with the 8bf2e754-2af1-4040-8bc5-68370794e367-1 09:13.504 --> 09:16.510 systems as well as the automations that are are are 8bf2e754-2af1-4040-8bc5-68370794e367-2 09:16.510 --> 09:17.840 running the laboratory. 26fd478a-2c64-4c8b-9347-7dae4c17ebaf-0 09:17.840 --> 09:22.269 So we're seeing a lot more in terms of integrations with 26fd478a-2c64-4c8b-9347-7dae4c17ebaf-1 09:22.269 --> 09:26.854 assets as well as the, the exchange of data in that space, 26fd478a-2c64-4c8b-9347-7dae4c17ebaf-2 09:26.854 --> 09:31.828 the human element of, of running laboratories, the analyst that 26fd478a-2c64-4c8b-9347-7dae4c17ebaf-3 09:31.828 --> 09:36.957 is an area that is very much top of mind now in primarily across, 26fd478a-2c64-4c8b-9347-7dae4c17ebaf-4 09:36.957 --> 09:41.542 you know, QC lab leaders is the, the human experience, the 26fd478a-2c64-4c8b-9347-7dae4c17ebaf-5 09:41.542 --> 09:46.204 analyst at the center of all this activity and how best are 26fd478a-2c64-4c8b-9347-7dae4c17ebaf-6 09:46.204 --> 09:51.256 they served and how best do they experience what is a lab of the 26fd478a-2c64-4c8b-9347-7dae4c17ebaf-7 09:51.256 --> 09:51.800 future? 01970df2-4f4c-4dd0-861e-02168be0ff93-0 09:52.360 --> 09:56.020 So essentially we, we could call it the connected analyst, but 01970df2-4f4c-4dd0-861e-02168be0ff93-1 09:56.020 --> 09:59.797 putting yourselves in the shoes of an analyst, how from the time 01970df2-4f4c-4dd0-861e-02168be0ff93-2 09:59.797 --> 10:03.283 that they arrive to work using NEMI bands or something like 01970df2-4f4c-4dd0-861e-02168be0ff93-3 10:03.283 --> 10:06.595 that to how they progress the entire day from scheduling 01970df2-4f4c-4dd0-861e-02168be0ff93-4 10:06.595 --> 10:10.372 automations, etcetera, all tied to the experience of the analyst 01970df2-4f4c-4dd0-861e-02168be0ff93-5 10:10.372 --> 10:11.360 in and of itself. 494d8aea-21fe-4ac9-9eef-4afc73310e8e-0 10:12.280 --> 10:15.839 And then as we progress through this, through this transition, 494d8aea-21fe-4ac9-9eef-4afc73310e8e-1 10:15.839 --> 10:17.760 we come to predictive operations. 10162147-47be-4d12-b7f7-c407e25d1b18-0 10:18.000 --> 10:21.815 And this is truly where you, we have the AI and ML advances and 10162147-47be-4d12-b7f7-c407e25d1b18-1 10:21.815 --> 10:25.510 all of the technologies that will essentially look at what is 10162147-47be-4d12-b7f7-c407e25d1b18-2 10:25.510 --> 10:28.908 data today, transition into information to knowledge, if 10162147-47be-4d12-b7f7-c407e25d1b18-3 10:28.908 --> 10:32.485 you're able to get to that place and then ultimately to, to 10162147-47be-4d12-b7f7-c407e25d1b18-4 10:32.485 --> 10:36.360 wisdom and have those predictive operations to support your lab. 5619b9cc-319f-417d-b5aa-750f5832702b-0 10:36.920 --> 10:40.350 So kind of we've bucketed we, we feel like this is, it's, it's 5619b9cc-319f-417d-b5aa-750f5832702b-1 10:40.350 --> 10:43.780 sort of a spectrum starting from the left with things that are 5619b9cc-319f-417d-b5aa-750f5832702b-2 10:43.780 --> 10:44.760 more foundational. 5c2d1bea-5f78-434c-95d7-7f2f971168d9-0 10:44.760 --> 10:48.121 And then as you advanced in terms of your maturity, you take 5c2d1bea-5f78-434c-95d7-7f2f971168d9-1 10:48.121 --> 10:50.160 out some more advanced capabilities. 2767a8da-999b-4983-8f44-425e69de0a38-0 10:51.040 --> 10:52.680 So I'll pause you and see any questions. 0da37b87-9c9c-40bb-9eff-0017da5749d3-0 10:59.280 --> 11:00.760 OK, I think this is frozen. ed3dae62-5c95-4b39-9fe0-98ca5dc511d7-0 11:02.120 --> 11:02.960 OK, there we go. a6c6d7cc-1103-4f46-b0eb-bb94e422589e-0 11:03.560 --> 11:07.040 So what does, what does a lab evolution look like? 5cac6e03-a876-4196-a4b0-652b6a00dc95-0 11:07.040 --> 11:10.524 If, if we build a, you know, ground ourselves in certain 5cac6e03-a876-4196-a4b0-652b6a00dc95-1 11:10.524 --> 11:14.376 trends that we see are going to impact the evolution of QC and 5cac6e03-a876-4196-a4b0-652b6a00dc95-2 11:14.376 --> 11:18.228 analytical laboratories in the near future, What does where do 5cac6e03-a876-4196-a4b0-652b6a00dc95-3 11:18.228 --> 11:18.840 you begin? cc7f4630-8591-417c-b816-9ae1c78deaca-0 11:18.840 --> 11:20.200 What does that really look like? b984908c-2205-4d5b-b298-f770a1f9e934-0 11:20.560 --> 11:24.756 And based on our more recent experience, also some of our b984908c-2205-4d5b-b298-f770a1f9e934-1 11:24.756 --> 11:29.025 dated experience, I would say that the the progress is not b984908c-2205-4d5b-b298-f770a1f9e934-2 11:29.025 --> 11:30.400 necessarily linear. 63c1045c-f771-47d3-91dd-d397b781d870-0 11:30.400 --> 11:32.760 So you don't have to start at the same place. ef3d4c6e-23db-47b9-bbd1-47d0f9e1582a-0 11:33.120 --> 11:37.040 And I think that's really important because you see bits ef3d4c6e-23db-47b9-bbd1-47d0f9e1582a-1 11:37.040 --> 11:40.960 and pieces of of work being done across different areas. 023a969f-7d26-48ea-909d-4da1877084a5-0 11:41.000 --> 11:44.604 You see a lot of times QC labs are embedded within 023a969f-7d26-48ea-909d-4da1877084a5-1 11:44.604 --> 11:48.844 manufacturing sites and you could see potentially labs take 023a969f-7d26-48ea-909d-4da1877084a5-2 11:48.844 --> 11:53.155 on different initiatives in order to advance what is the lab 023a969f-7d26-48ea-909d-4da1877084a5-3 11:53.155 --> 11:54.640 of the future vision. 37f0972a-7ebf-4add-b35f-be84651b4780-0 11:55.080 --> 11:58.872 So you could start with a focus on methods and we've, we are 37f0972a-7ebf-4add-b35f-be84651b4780-1 11:58.872 --> 12:02.539 seeing now the analytical transfers is probably one of the 37f0972a-7ebf-4add-b35f-be84651b4780-2 12:02.539 --> 12:04.840 biggest bottlenecks in the industry. 8fb2751a-86c7-4324-994f-408679640c91-0 12:04.840 --> 12:08.726 We've talked a lot about tech transfers and we're seeing that 8fb2751a-86c7-4324-994f-408679640c91-1 12:08.726 --> 12:12.675 momentum and energy being spent on analytical method transfers 8fb2751a-86c7-4324-994f-408679640c91-2 12:12.675 --> 12:16.248 because that's equally time consuming and, or often even 8fb2751a-86c7-4324-994f-408679640c91-3 12:16.248 --> 12:19.883 something that can become a gating factor in terms of how 8fb2751a-86c7-4324-994f-408679640c91-4 12:19.883 --> 12:21.200 quickly you can move. 28271555-1af3-483c-a95b-632f527d609e-0 12:22.440 --> 12:26.041 And if we go around this sort of circle here, and I would, I 28271555-1af3-483c-a95b-632f527d609e-1 12:26.041 --> 12:29.880 would take you from sort of left to right, really looking at the 28271555-1af3-483c-a95b-632f527d609e-2 12:29.880 --> 12:33.777 life cycle of a sample, the data that goes along with it, as well 28271555-1af3-483c-a95b-632f527d609e-3 12:33.777 --> 12:34.840 as the experience. ce2128ed-8680-4be8-aca1-6811dcd513fb-0 12:34.960 --> 12:36.600 This that's centered on the analyst. 0b2551b2-f2f4-433c-abc5-c6a9a2d6edf8-0 12:36.600 --> 12:40.181 So like 3 components here, the, the experience of the analyst, 0b2551b2-f2f4-433c-abc5-c6a9a2d6edf8-1 12:40.181 --> 12:43.477 what happens with the sample as well as all the data that 0b2551b2-f2f4-433c-abc5-c6a9a2d6edf8-2 12:43.477 --> 12:44.160 supports it. c123c7b0-1092-41e5-a697-daef848f9eaa-0 12:44.520 --> 12:48.030 Any of those components can, you could essentially choose to dive c123c7b0-1092-41e5-a697-daef848f9eaa-1 12:48.030 --> 12:49.200 in there and progress. 85100b75-6b3f-4c34-a409-28f623a6e72d-0 12:49.200 --> 12:52.628 So I'll, I'll dive into maybe digital life cycle management 85100b75-6b3f-4c34-a409-28f623a6e72d-1 12:52.628 --> 12:56.285 because it's top of mind for me and a client that I'm currently 85100b75-6b3f-4c34-a409-28f623a6e72d-2 12:56.285 --> 12:58.000 working with us on this topic. 55af4d89-6f08-4969-b682-a2b6750a1766-0 12:58.560 --> 13:00.560 I won't go in through all of that, go into all of them. d7f5daa2-c0a5-4794-ad67-fd711be2a07a-0 13:01.560 --> 13:07.327 So we have we, we see a bottleneck in the analytical d7f5daa2-c0a5-4794-ad67-fd711be2a07a-1 13:07.327 --> 13:08.960 transfer space. 48506411-b8c0-460a-8684-e70368517782-0 13:09.240 --> 13:14.941 We also see methods that are essentially legacy methods have 48506411-b8c0-460a-8684-e70368517782-1 13:14.941 --> 13:18.680 been up and running for 10/15/20 years. 7760ab2c-a2aa-4efb-a71d-07ec63973804-0 13:19.120 --> 13:23.095 We see the the constant battle between analysts choosing or 7760ab2c-a2aa-4efb-a71d-07ec63973804-1 13:23.095 --> 13:27.138 having to choose between the time they spend on transferring 7760ab2c-a2aa-4efb-a71d-07ec63973804-2 13:27.138 --> 13:30.451 new methods into the laboratories and or optimize 7760ab2c-a2aa-4efb-a71d-07ec63973804-3 13:30.451 --> 13:34.560 existing methods that have been around for a for a long time. b962e966-01f7-4cdc-922b-616a884d74b6-0 13:35.160 --> 13:39.931 And the big focus on data right now is essentially attempting to b962e966-01f7-4cdc-922b-616a884d74b6-1 13:39.931 --> 13:41.400 eliminate that need. ed564409-6c5a-4e0f-8edc-50596652a40c-0 13:41.400 --> 13:45.115 So you if you have the right data, data models and data ed564409-6c5a-4e0f-8edc-50596652a40c-1 13:45.115 --> 13:49.362 foundation to allow for methods to transition in as smoothly as ed564409-6c5a-4e0f-8edc-50596652a40c-2 13:49.362 --> 13:49.960 possible. 9fc12d52-3fad-4daf-8b0e-feace047a09f-0 13:50.280 --> 13:53.315 And the analysts are more focused on method modernization 9fc12d52-3fad-4daf-8b0e-feace047a09f-1 13:53.315 --> 13:56.560 on in terms of what is within the laboratories at the moment. 5be84203-53e3-4dba-b80d-86bad6fc3e73-0 13:56.920 --> 14:00.483 And so the focus on creating this loop where you could 5be84203-53e3-4dba-b80d-86bad6fc3e73-1 14:00.483 --> 14:04.434 accelerate transfers, create room for your existing analysts 5be84203-53e3-4dba-b80d-86bad6fc3e73-2 14:04.434 --> 14:08.451 to then focus on optimization and tie that back to R&D is 5be84203-53e3-4dba-b80d-86bad6fc3e73-3 14:08.451 --> 14:12.533 kind of where one of my clients is choosing to focus the area, 5be84203-53e3-4dba-b80d-86bad6fc3e73-4 14:12.533 --> 14:13.440 their efforts. c09fb639-165c-4d65-97ac-f6df243c7a2a-0 14:13.800 --> 14:17.821 And that is primarily from the perspective of having to manage, c09fb639-165c-4d65-97ac-f6df243c7a2a-1 14:17.821 --> 14:21.842 you know, challenges with talent and having to optimize kind of c09fb639-165c-4d65-97ac-f6df243c7a2a-2 14:21.842 --> 14:25.548 the way people are making decisions about where they spend c09fb639-165c-4d65-97ac-f6df243c7a2a-3 14:25.548 --> 14:26.240 their time. 59dfde58-02dc-45a0-94a2-063a60cd8c46-0 14:26.800 --> 14:28.440 I can go on to give you other examples. 94a8ec3f-2718-43bb-8555-3d74a97d6623-0 14:28.440 --> 14:30.080 I don't know, Lex, if you wanted to add to one of those. 2d53f25b-e73f-45ba-a25c-4fcfd10dd25b-0 14:30.080 --> 14:33.386 Yeah, I'll give another example about the common sort of 2d53f25b-e73f-45ba-a25c-4fcfd10dd25b-1 14:33.386 --> 14:36.692 objectives of goals that a number of companies are going 2d53f25b-e73f-45ba-a25c-4fcfd10dd25b-2 14:36.692 --> 14:38.200 out as real time business. ba6a74f4-fe60-46ab-b1d3-d1abb0f9bec7-0 14:38.480 --> 14:43.303 And in order to be able to get the real time business, being ba6a74f4-fe60-46ab-b1d3-d1abb0f9bec7-1 14:43.303 --> 14:48.048 able to get all the data but release those that have passed ba6a74f4-fe60-46ab-b1d3-d1abb0f9bec7-2 14:48.048 --> 14:52.240 the set limits becomes a critical component of that. 3a70b9c8-2634-4748-a4a7-05e051a6a73d-0 14:52.240 --> 14:56.112 And then when you when you want to review, when you want to 3a70b9c8-2634-4748-a4a7-05e051a6a73d-1 14:56.112 --> 14:59.726 allow for release to occur on the basis of prior tested 3a70b9c8-2634-4748-a4a7-05e051a6a73d-2 14:59.726 --> 15:03.921 materials or prior tested data, you want to be able to make sure 3a70b9c8-2634-4748-a4a7-05e051a6a73d-3 15:03.921 --> 15:05.600 that the data is accurate. 589eac8b-ac29-495f-bea4-0ca6ccad031b-0 15:05.600 --> 15:08.670 You want to be able to make sure that the models are accurate and 589eac8b-ac29-495f-bea4-0ca6ccad031b-1 15:08.670 --> 15:09.880 they're not hallucinating. e1589538-85db-4afd-a749-89a125d4921b-0 15:09.880 --> 15:13.541 So there's a whole host of stages of evolution that you go e1589538-85db-4afd-a749-89a125d4921b-1 15:13.541 --> 15:17.202 through to make review by exception a reality or real time e1589538-85db-4afd-a749-89a125d4921b-2 15:17.202 --> 15:18.320 reviews a reality. 7b9772c8-c2c1-46a7-bf07-b2eb432d65fe-0 15:19.560 --> 15:22.986 And that's another journey that we are working with another 7b9772c8-c2c1-46a7-bf07-b2eb432d65fe-1 15:22.986 --> 15:26.527 client where we are trying to get that data in place in a, in 7b9772c8-c2c1-46a7-bf07-b2eb432d65fe-2 15:26.527 --> 15:29.726 a way that we can actually release the Lord as the Lord 7b9772c8-c2c1-46a7-bf07-b2eb432d65fe-3 15:29.726 --> 15:31.040 gets ready for release. 97146398-9004-4b44-9c11-9a94f8b3d254-0 15:31.760 --> 15:36.004 The other, the other area I would say is there's been a lot 97146398-9004-4b44-9c11-9a94f8b3d254-1 15:36.004 --> 15:38.480 of emphasis on asset productivity. 1e7a1f11-67b6-49a6-8c76-16c27d7b7c51-0 15:39.760 --> 15:45.187 Many of the OEMs now are looking to provide data from the lab 1e7a1f11-67b6-49a6-8c76-16c27d7b7c51-1 15:45.187 --> 15:50.527 assets, lab equipment that are on the lab line available for 1e7a1f11-67b6-49a6-8c76-16c27d7b7c51-2 15:50.527 --> 15:55.692 utilization analysis, scheduling analysis and productivity 1e7a1f11-67b6-49a6-8c76-16c27d7b7c51-3 15:55.692 --> 15:56.480 analysis. af430af3-513e-43d1-ac1d-d42e21de0250-0 15:57.200 --> 16:01.763 Now depends on how all the equipment are in a certain af430af3-513e-43d1-ac1d-d42e21de0250-1 16:01.763 --> 16:07.002 equipment and certain types of assets may not be amenable for af430af3-513e-43d1-ac1d-d42e21de0250-2 16:07.002 --> 16:10.720 this kind of this kind of a transformation. 2670a052-a06c-4289-a9be-2e6c9c9da7b8-0 16:10.720 --> 16:14.704 But that's another area where as many of our clients are looking 2670a052-a06c-4289-a9be-2e6c9c9da7b8-1 16:14.704 --> 16:18.259 to build new labs or buy new equipment, They're trying to 2670a052-a06c-4289-a9be-2e6c9c9da7b8-2 16:18.259 --> 16:22.304 build in the extraction of those data elements from the assets so 2670a052-a06c-4289-a9be-2e6c9c9da7b8-3 16:22.304 --> 16:26.166 they can actually plan better and, and, and, and improve their 2670a052-a06c-4289-a9be-2e6c9c9da7b8-4 16:26.166 --> 16:27.760 asset efficiencies better. 2b7acdc1-d6d6-44b2-adfd-aeb3971e9c88-0 16:27.760 --> 16:31.726 So as Iknum said, you can begin the journey from anyone 2b7acdc1-d6d6-44b2-adfd-aeb3971e9c88-1 16:31.726 --> 16:35.479 dimension or multiple dimensions, but it's important 2b7acdc1-d6d6-44b2-adfd-aeb3971e9c88-2 16:35.479 --> 16:39.800 to have that clarity of what are you going to get out of it. 53d8f919-a7e6-46f0-be08-6a25b0e061aa-0 16:39.800 --> 16:42.021 And that's something that we will talk about later in the 53d8f919-a7e6-46f0-be08-6a25b0e061aa-1 16:42.021 --> 16:42.520 talk as well. 674dd15d-e924-4bc5-8c6c-2b11119ebce4-0 16:42.920 --> 16:44.040 Yeah, thank you for that. 3f846388-fe98-4a43-93c3-e0806ae3fcc0-0 16:44.600 --> 16:46.160 So I mean, I, I guess, yeah, go ahead. 1fb450a4-77fb-4bfe-8e78-5fb10dbbdee5-0 16:46.480 --> 16:48.080 Yeah, he's going to get a mic. c143c654-9b89-4d90-a121-b40d48ec793d-0 16:48.120 --> 16:51.811 I think he's going to get a thank you for that because I c143c654-9b89-4d90-a121-b40d48ec793d-1 16:51.811 --> 16:53.560 think they're recording it. da0a7e3f-8b33-479f-b263-bb7e847c5e4e-0 16:53.560 --> 16:54.840 So they want the questions to. 39630ce1-0d17-46c3-a53f-34aa3e5de8c3-0 16:55.280 --> 16:56.000 Yeah, thank you. 0f95e0ef-5800-41f0-854b-a980d45f5ecb-0 16:57.080 --> 16:58.320 So I've got a question. 96d3084f-c7a9-4d54-bbac-274c7d6c783c-0 16:58.320 --> 17:03.190 How is the readiness of the pharmaceutical companies to 96d3084f-c7a9-4d54-bbac-274c7d6c783c-1 17:03.190 --> 17:07.800 implement those analytical tools like EAT and so on? b4bf0035-19ee-4672-bb49-29edb632823f-0 17:08.600 --> 17:09.440 That's a great question. f8bf415c-6f65-4f2f-a90e-cfdf32f3a11b-0 17:09.440 --> 17:13.929 So I'll, I'll steal from my own statement that I was going to f8bf415c-6f65-4f2f-a90e-cfdf32f3a11b-1 17:13.929 --> 17:18.273 end the talk with, which is, which is, you know, the famous f8bf415c-6f65-4f2f-a90e-cfdf32f3a11b-2 17:18.273 --> 17:22.618 William Gibson quote that says the future is here, but it's f8bf415c-6f65-4f2f-a90e-cfdf32f3a11b-3 17:22.618 --> 17:25.080 just unevenly distributed, right? 1bbe5316-6fb0-4921-9017-786c372cbe4c-0 17:25.080 --> 17:29.913 So there are pockets of there are pockets of innovation and 1bbe5316-6fb0-4921-9017-786c372cbe4c-1 17:29.913 --> 17:34.988 readiness in different parts of the manufacturer, in different 1bbe5316-6fb0-4921-9017-786c372cbe4c-2 17:34.988 --> 17:39.178 in different parts of the portfolio of sites, labs, 1bbe5316-6fb0-4921-9017-786c372cbe4c-3 17:39.178 --> 17:42.320 assets, products that our clients see. 51238d3d-4b47-4521-9b6e-935db8a697e1-0 17:42.600 --> 17:47.262 And so is every single product ready amenable for this kind of 51238d3d-4b47-4521-9b6e-935db8a697e1-1 17:47.262 --> 17:48.520 a transformation? abde4355-8ddc-4f30-a779-5ee2d3a0b00c-0 17:48.520 --> 17:49.680 The answer is likely not. 3bcaa002-01f5-4330-99aa-87c81ed267b7-0 17:50.680 --> 17:55.026 You need to have the right case to be able to invest in either a 3bcaa002-01f5-4330-99aa-87c81ed267b7-1 17:55.026 --> 17:59.238 process analytical tool or the data connectivity from the labs 3bcaa002-01f5-4330-99aa-87c81ed267b7-2 17:59.238 --> 18:02.715 all the way up to the manufacturing process and tie 3bcaa002-01f5-4330-99aa-87c81ed267b7-3 18:02.715 --> 18:04.120 all of them together. c977b56d-1028-4047-b4b4-8a6925688744-0 18:04.880 --> 18:08.116 The business case has to make sense, but the technologies c977b56d-1028-4047-b4b4-8a6925688744-1 18:08.116 --> 18:09.400 exist, the data exists. 52255feb-63b7-4797-849c-a3ee2d93f984-0 18:09.920 --> 18:14.010 The the the the business case, again depending on the product 52255feb-63b7-4797-849c-a3ee2d93f984-1 18:14.010 --> 18:16.320 and the asset and what not exists. efdbff46-3f2f-42b4-bb8a-579e375987df-0 18:17.160 --> 18:20.307 What what tends to happen is there are many choices that you efdbff46-3f2f-42b4-bb8a-579e375987df-1 18:20.307 --> 18:22.320 have in front of you how to get there. afbf60db-e0e7-4b45-b326-499037626112-0 18:22.680 --> 18:26.040 And that's that's really where the challenge lies is like what afbf60db-e0e7-4b45-b326-499037626112-1 18:26.040 --> 18:29.400 path works for one company would not work for another company. 77c498b9-4d9f-4e84-bf8b-b790e2fdd6f4-0 18:29.400 --> 18:32.355 And many of our clients are very used to benchmarking amongst 77c498b9-4d9f-4e84-bf8b-b790e2fdd6f4-1 18:32.355 --> 18:32.880 each other. e99b0518-42f7-4e28-8635-33618229b4f6-0 18:33.200 --> 18:36.457 And the challenge is they all come from different legacy of e99b0518-42f7-4e28-8635-33618229b4f6-1 18:36.457 --> 18:39.877 how they came about their their own network of labs, their own e99b0518-42f7-4e28-8635-33618229b4f6-2 18:39.877 --> 18:40.800 network of sites. 85346e9d-a312-4d84-9b46-b74dc02ff2bd-0 18:41.280 --> 18:42.480 Hopefully that answers the question. 2a18e9e5-f035-416f-850f-8eca2822aad1-0 18:42.480 --> 18:43.560 I don't know if you have anything more. 06b1b6ea-defa-4c10-a23b-8a1ccf0ef560-0 18:43.720 --> 18:43.880 Yeah. 47ccd5e0-ac73-4926-ad4a-ef557ad2774d-0 18:43.920 --> 18:47.894 The only thing I would I would add to that is I think the 47ccd5e0-ac73-4926-ad4a-ef557ad2774d-1 18:47.894 --> 18:52.280 readiness is, is, is more about where they focus their efforts. 01246985-1899-4c01-91ba-e9b5f9a9f7ff-0 18:52.280 --> 18:55.501 And So what I would say is with with majority of my clients 01246985-1899-4c01-91ba-e9b5f9a9f7ff-1 18:55.501 --> 18:58.776 across across major farmers, if you introduce the concept of 01246985-1899-4c01-91ba-e9b5f9a9f7ff-2 18:58.776 --> 19:02.267 BAT, what they will tell you is we're doing it here and here and 01246985-1899-4c01-91ba-e9b5f9a9f7ff-3 19:02.267 --> 19:04.200 here in these sites on these lines. 73ff53f8-e05f-437d-b2a2-17f7b3b29aad-0 19:04.440 --> 19:05.120 So they're ready. 0a55036b-052b-482b-a303-995234c4dd17-0 19:05.120 --> 19:07.820 It just like where does it make sense to make that investment is 0a55036b-052b-482b-a303-995234c4dd17-1 19:07.820 --> 19:08.360 the question. d6fe275f-6a8b-43f9-995f-d8ce89a2667d-0 19:08.360 --> 19:11.520 So I would, I'd be surprised if folks are not using it at all. f7b88d81-fd99-4d66-9b3f-0ebad1c4f2f5-0 19:12.160 --> 19:14.714 They're all they're definitely using it in some way, shape or f7b88d81-fd99-4d66-9b3f-0ebad1c4f2f5-1 19:14.714 --> 19:14.920 form. 52960b5c-6ea5-40c8-88d5-d2d32eb8aa4d-0 19:14.920 --> 19:16.880 It's just the capacity depends. c0aac22d-00be-47a8-95a7-b6227150b129-0 19:16.880 --> 19:20.337 And how do you scale it becomes a question in terms of c0aac22d-00be-47a8-95a7-b6227150b129-1 19:20.337 --> 19:21.280 prioritization. 0c0dc984-60c6-4c9c-9f43-8475ebaa2cda-0 19:23.280 --> 19:24.760 Can I ask a question as well? b9a619b6-deca-44b6-8dfa-9a3d784823cd-0 19:25.280 --> 19:29.531 I'm a former analytical chemist myself, so I'm interested in b9a619b6-deca-44b6-8dfa-9a3d784823cd-1 19:29.531 --> 19:29.880 this. 0d94b692-4cc8-42df-8409-cb43c281d15d-0 19:30.120 --> 19:35.970 But in my experience, there's a lot of time that's involved with 0d94b692-4cc8-42df-8409-cb43c281d15d-1 19:35.970 --> 19:37.680 lab investigations. 867b3c70-ad61-441f-9bbf-603505b249dd-0 19:37.760 --> 19:39.240 Is that and part of this as well. 339f21e0-a3af-4bad-a5bf-cb03daa2ca70-0 19:39.480 --> 19:39.720 Yeah. ad7477b6-4d73-44bf-961e-01a250d79f2b-0 19:39.960 --> 19:43.575 So the review by exception is tied with the investigations ad7477b6-4d73-44bf-961e-01a250d79f2b-1 19:43.575 --> 19:47.374 around the the deviations that you that occurs in outer specs ad7477b6-4d73-44bf-961e-01a250d79f2b-2 19:47.374 --> 19:51.112 or out of, you know, equipment, equipment deviations, sample ad7477b6-4d73-44bf-961e-01a250d79f2b-3 19:51.112 --> 19:54.605 deviations, step deviations, you know, and environmental ad7477b6-4d73-44bf-961e-01a250d79f2b-4 19:54.605 --> 19:55.280 deviations. eabfa7bc-79b5-4915-9dea-e996f3b09752-0 19:55.800 --> 19:56.280 Exactly. 0729f965-8e27-490b-90a1-4c09cca1a535-0 19:56.320 --> 19:56.480 Yeah. 2e296135-b216-4490-8902-99093df24c2c-0 19:57.440 --> 20:01.810 So I'm a process architect and been doing lab designs for 20 2e296135-b216-4490-8902-99093df24c2c-1 20:01.810 --> 20:02.240 years. e5607120-ed6f-4225-ae53-9c35744edab9-0 20:02.240 --> 20:07.054 So, so, so big challenge we run into and it's very prominent in e5607120-ed6f-4225-ae53-9c35744edab9-1 20:07.054 --> 20:07.280 Rd. ae212755-0ca7-4492-a63c-c32f9d952141-0 20:07.320 --> 20:09.360 labs, not as much in QC. a5b4986a-ed00-49ab-b36c-b4c6bc1cfd9a-0 20:09.840 --> 20:13.800 You know, you see equipment and newer equipment is coming. 5ab137f2-e25a-49a3-a047-2b5b7514016b-0 20:14.320 --> 20:18.480 For the same lab, you have more automation coming, but then you 5ab137f2-e25a-49a3-a047-2b5b7514016b-1 20:18.480 --> 20:22.315 have this older equipment sitting there and there's such a 5ab137f2-e25a-49a3-a047-2b5b7514016b-2 20:22.315 --> 20:25.760 great resistance by the scientist to let go of that. 546c3e1f-5a1b-495a-a49f-4e4a04b31ce1-0 20:26.160 --> 20:31.406 And we've been pushing very strongly, you know, some kind of 546c3e1f-5a1b-495a-a49f-4e4a04b31ce1-1 20:31.406 --> 20:36.480 utilization tracking and even asset management scheduling. c9742cf2-22bb-4cab-8823-360709e9e1b9-0 20:36.640 --> 20:40.466 We have piloted several programs, but there's a strong c9742cf2-22bb-4cab-8823-360709e9e1b9-1 20:40.466 --> 20:44.640 resistance and we have just not been able to be successful. 3d75568e-1676-4ef5-a0fe-d62dd10938e6-0 20:44.640 --> 20:48.459 Like you have so many projects, you know, and the client can 3d75568e-1676-4ef5-a0fe-d62dd10938e6-1 20:48.459 --> 20:52.090 see, like the management can see that this demand for new 3d75568e-1676-4ef5-a0fe-d62dd10938e6-2 20:52.090 --> 20:55.909 equipment should really go hand in hand with, you know, some 3d75568e-1676-4ef5-a0fe-d62dd10938e6-3 20:55.909 --> 20:57.599 equipment being phased out. f7afbc59-bf6e-4e17-b8ef-29efc000903a-0 20:57.600 --> 20:59.560 But that's just not happening actually. 3dd5c716-8ab5-4cc5-a880-034323fc63b3-0 20:59.560 --> 21:03.040 In fact, I'll add one more complexity to what you said. ce2f4903-7f4b-48a9-839b-98f595f7f845-0 21:03.320 --> 21:06.491 I've had a client that was trying to tech transfer out an ce2f4903-7f4b-48a9-839b-98f595f7f845-1 21:06.491 --> 21:09.280 analytical method from one site to the other site. e00f6bd8-316d-4c39-97e4-7aba3b0e48fd-0 21:09.600 --> 21:12.286 They had the exact same equipment but couldn't reproduce e00f6bd8-316d-4c39-97e4-7aba3b0e48fd-1 21:12.286 --> 21:14.831 the result, right, because of whatever feature of the e00f6bd8-316d-4c39-97e4-7aba3b0e48fd-2 21:14.831 --> 21:17.470 equipment that they had and, and feature sets and local e00f6bd8-316d-4c39-97e4-7aba3b0e48fd-3 21:17.470 --> 21:19.120 conditions and so on and so forth. 8cf189e6-e9a7-4cfe-8089-ca1758a54c1e-0 21:19.120 --> 21:24.002 So that analyst preference or lab analyst preference for 8cf189e6-e9a7-4cfe-8089-ca1758a54c1e-1 21:24.002 --> 21:28.800 equipment process, so on and so forth plays a big role. 53b511aa-ab17-46e2-bb8a-51b4b3e39028-0 21:30.360 --> 21:35.466 There's 2 two or three things that we've seen kind of surmount 53b511aa-ab17-46e2-bb8a-51b4b3e39028-1 21:35.466 --> 21:40.086 that barrier #1 is the, you know, the, the, the case for 53b511aa-ab17-46e2-bb8a-51b4b3e39028-2 21:40.086 --> 21:44.788 change with respect to the amount of whether it's time or 53b511aa-ab17-46e2-bb8a-51b4b3e39028-3 21:44.788 --> 21:48.760 cycle time associated with getting the test out. 80aa77d9-52ec-4a1d-8562-6f54fa56ddba-0 21:49.000 --> 21:51.650 And there are commercial pressures and you don't have 80aa77d9-52ec-4a1d-8562-6f54fa56ddba-1 21:51.650 --> 21:54.645 that much time it takes, it takes a lot of effort to kind of 80aa77d9-52ec-4a1d-8562-6f54fa56ddba-2 21:54.645 --> 21:56.560 get that, meet that, meet that burden. 2af8de2d-65bd-4013-90a6-5c35436926d6-0 21:56.840 --> 22:00.120 The second is just volume of volume of work. 9e012760-216d-4c8b-b67f-ac6764ec1623-0 22:01.080 --> 22:04.022 We've seen that when you have significant volume of work in 9e012760-216d-4c8b-b67f-ac6764ec1623-1 22:04.022 --> 22:07.111 year and pretty much most of our clients and it's different in 9e012760-216d-4c8b-b67f-ac6764ec1623-2 22:07.111 --> 22:10.201 R&D where it's different in commercial tests where you you 9e012760-216d-4c8b-b67f-ac6764ec1623-3 22:10.201 --> 22:12.800 have very limited budgetary increase year over year. 87af8b37-02d2-470b-a73f-877fe7d08b49-0 22:13.160 --> 22:16.880 So doing more for less becomes a lever for action. 3a2bc963-e2be-4111-9001-a755cca6f769-0 22:17.480 --> 22:19.640 But at the end of the day, there's executive sponsorship. 26eb2750-d25b-4fb1-a9bb-a453c250f705-0 22:19.640 --> 22:22.357 We always lean on an understanding with senior 26eb2750-d25b-4fb1-a9bb-a453c250f705-1 22:22.357 --> 22:25.595 leaders who can drive that change and with at different 26eb2750-d25b-4fb1-a9bb-a453c250f705-2 22:25.595 --> 22:29.006 levels in the organization leaders almost like key opinion 26eb2750-d25b-4fb1-a9bb-a453c250f705-3 22:29.006 --> 22:31.840 leaders that demonstrate how that change occurs. a1e075ff-1443-4d85-a96c-313612367094-0 22:31.840 --> 22:35.066 There was a client where we actually use the lab analysts a1e075ff-1443-4d85-a96c-313612367094-1 22:35.066 --> 22:38.738 who are using the limb system to kind of make the case for change a1e075ff-1443-4d85-a96c-313612367094-2 22:38.738 --> 22:40.240 for the other lab analysts. aec7bb55-c899-4d04-b844-48747065623a-0 22:40.840 --> 22:43.868 So using their peers, key opinion leaders who they trust aec7bb55-c899-4d04-b844-48747065623a-1 22:43.868 --> 22:47.215 as key opinion leaders goes a long way in kind of changing the aec7bb55-c899-4d04-b844-48747065623a-2 22:47.215 --> 22:47.640 mindset. d1cfa037-a27e-4bbf-a5cf-8ec91124c13b-0 22:47.640 --> 22:52.373 If you guess to me, I mean, you know, you have so much like IoT d1cfa037-a27e-4bbf-a5cf-8ec91124c13b-1 22:52.373 --> 22:57.255 and we have so many smart assets now, but we're still not able to d1cfa037-a27e-4bbf-a5cf-8ec91124c13b-2 22:57.255 --> 23:01.841 connect all the yeah, we're not able to affect this need for, d1cfa037-a27e-4bbf-a5cf-8ec91124c13b-3 23:01.841 --> 23:05.983 you know, there's just a constant need for space, which d1cfa037-a27e-4bbf-a5cf-8ec91124c13b-4 23:05.983 --> 23:10.199 to me is, I don't know, I don't think it's really there. 7da218b6-3d90-4a00-80a8-a1ee1e3ffeaa-0 23:10.960 --> 23:14.195 And I think I mean you're on to something which is which we 7da218b6-3d90-4a00-80a8-a1ee1e3ffeaa-1 23:14.195 --> 23:17.376 haven't listed here, but the, the culture component of any 7da218b6-3d90-4a00-80a8-a1ee1e3ffeaa-2 23:17.376 --> 23:20.558 transformation that you're trying to drive and how best do 7da218b6-3d90-4a00-80a8-a1ee1e3ffeaa-3 23:20.558 --> 23:23.793 you change the culture to be willing to let go of something 7da218b6-3d90-4a00-80a8-a1ee1e3ffeaa-4 23:23.793 --> 23:27.244 that is familiar or an Oreo you go to is very much in the realm 7da218b6-3d90-4a00-80a8-a1ee1e3ffeaa-5 23:27.244 --> 23:29.239 of like effective change management. ddb424e6-90d2-4ab6-bec1-eefb3e183b3c-0 23:29.600 --> 23:33.825 And I think that there is a lot of focus on laboratories right ddb424e6-90d2-4ab6-bec1-eefb3e183b3c-1 23:33.825 --> 23:38.117 now, both R&D and and QC and and QC kind of in a commercial ddb424e6-90d2-4ab6-bec1-eefb3e183b3c-2 23:38.117 --> 23:38.520 space. 2d1ed5e1-da4f-46d7-8002-9d144246008b-0 23:38.800 --> 23:41.488 So I do think this conversation and like why do you hold on to 2d1ed5e1-da4f-46d7-8002-9d144246008b-1 23:41.488 --> 23:44.090 something like what's truly the benefit of doing that versus 2d1ed5e1-da4f-46d7-8002-9d144246008b-2 23:44.090 --> 23:44.560 letting go? 6a44c365-61e5-43ae-9078-71dfc7fa3e80-0 23:44.560 --> 23:47.918 Because there are either faster or more advanced equipment 6a44c365-61e5-43ae-9078-71dfc7fa3e80-1 23:47.918 --> 23:51.163 that's going to become a big component as well as you go 6a44c365-61e5-43ae-9078-71dfc7fa3e80-2 23:51.163 --> 23:53.440 through just different transformations. f6669d09-bcd2-441d-9dd5-18f73430c37e-0 23:57.480 --> 23:58.520 Sorry, we have two minutes left. 158ffce0-acda-419c-811b-5b4f8a27de48-0 23:58.520 --> 24:00.680 Yeah, so I know we just have a little bit of more time. a1044e4e-bf0c-4c05-9ae6-4069e3f060e8-0 24:00.680 --> 24:03.200 So I want to maybe thank you for those questions. 09816d42-1b3d-48d0-a6bf-1d7cc4b2f1ac-0 24:03.200 --> 24:05.958 By the way, that was really, really good to have that 09816d42-1b3d-48d0-a6bf-1d7cc4b2f1ac-1 24:05.958 --> 24:06.520 discussion. 838444f6-67fe-4646-a13a-c57e4a2bb447-0 24:06.800 --> 24:10.131 I want to maybe just take us to a closing, which is kind of the 838444f6-67fe-4646-a13a-c57e4a2bb447-1 24:10.131 --> 24:13.567 journey now that we know kind of what are the different areas you 838444f6-67fe-4646-a13a-c57e4a2bb447-2 24:13.567 --> 24:16.794 could essentially dabble with when it comes to thinking about 838444f6-67fe-4646-a13a-c57e4a2bb447-3 24:16.794 --> 24:18.200 what is allowed the future. 92817097-92a1-474a-aac1-ce5b8c43643a-0 24:19.440 --> 24:22.652 I think the journey that an organization chooses to take can 92817097-92a1-474a-aac1-ce5b8c43643a-1 24:22.652 --> 24:23.600 be very different. d5414fdd-7fb7-4099-8b58-12f826d3ac9c-0 24:23.680 --> 24:26.359 We've kind of laid out three components here, but you could d5414fdd-7fb7-4099-8b58-12f826d3ac9c-1 24:26.359 --> 24:28.280 be anywhere in the middle of the spectrum. 5a005d0a-4824-4919-8478-04651646d270-0 24:28.800 --> 24:32.140 And so like from an industry perspective, if you were to 5a005d0a-4824-4919-8478-04651646d270-1 24:32.140 --> 24:35.715 think of this, you know, cell and gene therapy organizations 5a005d0a-4824-4919-8478-04651646d270-2 24:35.715 --> 24:39.525 that are, you know, bringing up operations may, may necessarily, 5a005d0a-4824-4919-8478-04651646d270-3 24:39.525 --> 24:42.924 it might be necessary for them to take this leap leapfrog 5a005d0a-4824-4919-8478-04651646d270-4 24:42.924 --> 24:46.675 approach where you're trying to be aggressive, You want to test 5a005d0a-4824-4919-8478-04651646d270-5 24:46.675 --> 24:50.192 out different concepts and you kind of want to move forward 5a005d0a-4824-4919-8478-04651646d270-6 24:50.192 --> 24:53.240 with a very kind of leapfrog approach, so to speak. d7d8bc9d-18b6-48ce-85cf-8687f238c53f-0 24:53.640 --> 24:56.200 And then there can be organizations that have been d7d8bc9d-18b6-48ce-85cf-8687f238c53f-1 24:56.200 --> 24:58.810 running and, and maintaining QC labs and, you know, d7d8bc9d-18b6-48ce-85cf-8687f238c53f-2 24:58.810 --> 25:01.120 pharmaceutical manufacturing for a long time. b08d4587-8aa4-47d0-ae88-4203cea1c89a-0 25:01.400 --> 25:05.362 It's also very common to see organizations to choose to focus b08d4587-8aa4-47d0-ae88-4203cea1c89a-1 25:05.362 --> 25:07.280 on what are core capabilities. 51ea7165-a2c2-4901-99ed-2a72d74d29a4-0 25:07.680 --> 25:11.000 And you know, that may be something as simple as we need 51ea7165-a2c2-4901-99ed-2a72d74d29a4-1 25:11.000 --> 25:14.496 to upgrade our limbs and LES before we can even think about 51ea7165-a2c2-4901-99ed-2a72d74d29a4-2 25:14.496 --> 25:18.049 anything more advanced because so much depends on it that we 51ea7165-a2c2-4901-99ed-2a72d74d29a4-3 25:18.049 --> 25:21.894 need to spend time on that first and then gradually kind of build 51ea7165-a2c2-4901-99ed-2a72d74d29a4-4 25:21.894 --> 25:24.400 on what would be more advanced capability. ba450a85-eb7d-47d1-9811-8be4db1a56d6-0 25:24.400 --> 25:27.236 So leading to this continuous improvement path and then all ba450a85-eb7d-47d1-9811-8be4db1a56d6-1 25:27.236 --> 25:28.560 the combinations in between. 0620e7c8-382e-4276-803c-40e33102e5bd-0 25:28.560 --> 25:31.869 So the message I want to leave everybody with this slide is 0620e7c8-382e-4276-803c-40e33102e5bd-1 25:31.869 --> 25:35.454 simply that the journey that an organization is choosing to take 0620e7c8-382e-4276-803c-40e33102e5bd-2 25:35.454 --> 25:38.928 can be so different depending on the conditions as well as the 0620e7c8-382e-4276-803c-40e33102e5bd-3 25:38.928 --> 25:41.080 drivers for choosing a transformation. 93fbc3dd-b7a9-4d6a-a49d-399ca89c60bc-0 25:41.840 --> 25:45.065 And we see pretty much well within the spectrum is where 93fbc3dd-b7a9-4d6a-a49d-399ca89c60bc-1 25:45.065 --> 25:47.160 most of our clients are are falling. f27ac6bf-19c8-4c8a-9dc3-23c623ef027e-0 25:51.120 --> 25:55.880 I could just maybe, yeah, I'll just close, I'll just close. c19cd8cd-4052-462f-8a42-3b2c75c71925-0 25:55.880 --> 25:58.137 This slide just talks about kind of, you know, as you think about c19cd8cd-4052-462f-8a42-3b2c75c71925-1 25:58.137 --> 25:58.240 it. 2e430e3f-32eb-4525-a9ce-853985a37164-0 25:58.240 --> 26:02.105 And I was making the comment earlier that the future is here, 2e430e3f-32eb-4525-a9ce-853985a37164-1 26:02.105 --> 26:02.480 right? 1ae1074b-c078-4bcd-bc19-4b9a37cc97f7-0 26:02.520 --> 26:03.880 But it's just unevenly distributed. 87112ab7-3c3b-4ff7-a73d-0b5ab8be50a1-0 26:03.880 --> 26:07.708 But I also don't like that, that, that, that, that phrase 87112ab7-3c3b-4ff7-a73d-0b5ab8be50a1-1 26:07.708 --> 26:11.470 or, or sentence from William Gibson, because it actually 87112ab7-3c3b-4ff7-a73d-0b5ab8be50a1-2 26:11.470 --> 26:15.629 takes away agency from people because if you feel like you can 87112ab7-3c3b-4ff7-a73d-0b5ab8be50a1-3 26:15.629 --> 26:19.523 actually, you know, somebody else is doing the innovation, 87112ab7-3c3b-4ff7-a73d-0b5ab8be50a1-4 26:19.523 --> 26:19.920 right? c6ddd021-f278-4e74-a81a-85ed0ae2b0ad-0 26:20.160 --> 26:21.680 Because it's like, I'm, I don't need to do it. 3a81ddb2-af7c-4acf-9d2c-59a535ac185b-0 26:21.680 --> 26:22.560 I just need to borrow it. db91ec46-f223-41ff-88c3-cfc8a49e8c69-0 26:22.560 --> 26:25.782 But there is there is agency involved in pulling that db91ec46-f223-41ff-88c3-cfc8a49e8c69-1 26:25.782 --> 26:29.421 together and the journeys that that Iknam talked about to to db91ec46-f223-41ff-88c3-cfc8a49e8c69-2 26:29.421 --> 26:33.061 constitute the journey that suits for your situation for for db91ec46-f223-41ff-88c3-cfc8a49e8c69-3 26:33.061 --> 26:36.701 your specific situation and what drives that is the business db91ec46-f223-41ff-88c3-cfc8a49e8c69-4 26:36.701 --> 26:37.000 case. a4bb895c-5f26-4b98-85e8-aa6d27319b2d-0 26:37.040 --> 26:40.631 And when you and we talked to many of our clients, the three a4bb895c-5f26-4b98-85e8-aa6d27319b2d-1 26:40.631 --> 26:43.929 simple business cases that people go with one, the most a4bb895c-5f26-4b98-85e8-aa6d27319b2d-2 26:43.929 --> 26:47.579 fundamental 1 and that that we recommend always is around the a4bb895c-5f26-4b98-85e8-aa6d27319b2d-3 26:47.579 --> 26:50.877 time to market because fundamentally what we are trying a4bb895c-5f26-4b98-85e8-aa6d27319b2d-4 26:50.877 --> 26:54.115 to do with any pharmaceutical product is to get to the a4bb895c-5f26-4b98-85e8-aa6d27319b2d-5 26:54.115 --> 26:55.999 patients as quickly as possible. a68eaecc-6b55-4324-93bb-b44477961030-0 26:56.320 --> 27:00.646 That frames the, the, the, the, the, the, the most preferred a68eaecc-6b55-4324-93bb-b44477961030-1 27:00.646 --> 27:01.640 business case. 56e929bd-53c9-41d1-ae65-cc7e9364d34d-0 27:01.640 --> 27:05.004 But it's also the hardest to kind of tie it to labs 56e929bd-53c9-41d1-ae65-cc7e9364d34d-1 27:05.004 --> 27:09.275 specifically because labs is but one component of the overarching 56e929bd-53c9-41d1-ae65-cc7e9364d34d-2 27:09.275 --> 27:12.640 value chain in, in delivering products to patients. 7721b756-2a29-4059-8079-783388d86cb0-0 27:13.360 --> 27:16.715 The second one obviously is what we used to call in, in the 7721b756-2a29-4059-8079-783388d86cb0-1 27:16.715 --> 27:19.120 consulting parlance, doing more with less. 41ed0fde-03e8-4882-9c69-dbcc49f55e69-0 27:20.280 --> 27:22.991 Sure, you could do more with less, but what I have kind of 41ed0fde-03e8-4882-9c69-dbcc49f55e69-1 27:22.991 --> 27:25.840 amended that with my clients to say is doing quick with less. 6079dd52-ad55-4369-b567-2eca0f5ec88e-0 27:26.320 --> 27:30.492 Can we can we get to the quicker ways of working on and 6079dd52-ad55-4369-b567-2eca0f5ec88e-1 27:30.492 --> 27:35.038 delivering you know lab results etcetera whatever it is, you 6079dd52-ad55-4369-b567-2eca0f5ec88e-2 27:35.038 --> 27:38.987 know release product quicker with the less amount of 6079dd52-ad55-4369-b567-2eca0f5ec88e-3 27:38.987 --> 27:43.160 resources that you can expend in in the company, right. 8c08ccbd-1609-4452-bbe2-866eb398cb89-0 27:43.160 --> 27:44.680 So that's that's the second business case. 550b2a0b-9c14-4c53-ba5c-0f892536207c-0 27:44.680 --> 27:47.507 The third business case is obviously efficiencies, you 550b2a0b-9c14-4c53-ba5c-0f892536207c-1 27:47.507 --> 27:50.540 know, year over year you have expectations of efficiencies 550b2a0b-9c14-4c53-ba5c-0f892536207c-2 27:50.540 --> 27:53.778 from from leaders in in pharma operations and how you actually 550b2a0b-9c14-4c53-ba5c-0f892536207c-3 27:53.778 --> 27:56.400 maintain and meet those commitments of efficiency. b9477a7d-eb06-442b-9cf4-807ecbdff38d-0 27:56.400 --> 27:59.960 So the business case kind of falls in one of those. 4d42a97b-3867-4fd6-8f9d-a12bdbb31ef6-0 27:59.960 --> 28:04.897 And what I, what I typically see many of our clients look for is 4d42a97b-3867-4fd6-8f9d-a12bdbb31ef6-1 28:04.897 --> 28:06.720 what are my peers doing. a36ba4f7-aafa-4867-a7b8-fc67885a5686-0 28:07.000 --> 28:10.004 And a lot of the times I actually advise our clients to a36ba4f7-aafa-4867-a7b8-fc67885a5686-1 28:10.004 --> 28:13.491 kind of chart your own journey, right, because as much as people a36ba4f7-aafa-4867-a7b8-fc67885a5686-2 28:13.491 --> 28:16.817 like to look at benchmarks and what others are doing, it only a36ba4f7-aafa-4867-a7b8-fc67885a5686-3 28:16.817 --> 28:18.480 gets you part of the way there. fdfbfa19-b4a7-4f48-88a2-67dd92136f43-0 28:18.880 --> 28:23.008 So anyway, with that, I'll open it up for any final questions, fdfbfa19-b4a7-4f48-88a2-67dd92136f43-1 28:23.008 --> 28:25.040 but that's the end of our talk. 857069c0-0644-4dfb-af8a-efb193b31db2-0 28:25.040 --> 28:27.280 You can look us up and connect with us. 1988e508-8446-410b-bc25-998cec0af1a1-0 28:27.280 --> 28:31.289 We are hanging out in the exhibition area, but we're also 1988e508-8446-410b-bc25-998cec0af1a1-1 28:31.289 --> 28:32.880 here in the conference. 0278bd8a-4062-4e80-9fbb-3fb337203fcf-0 28:33.600 --> 28:34.000 Thank you. 077c1473-1756-4e23-b6d2-13ddd697da8c-0 28:36.760 --> 28:38.869 There's a, there seems to be a question there in the corner 077c1473-1756-4e23-b6d2-13ddd697da8c-1 28:38.869 --> 28:39.080 there. fcd74f27-4aec-4cc4-80ab-02221e538a02-0 28:41.720 --> 28:42.560 I think he has a question. f1eba4ec-dda2-4589-8a65-2978df61f397-0 29:00.200 --> 29:01.640 Hi, thank you for your presentation. 65e87660-d3c6-4eb2-9110-da70e7119213-0 29:01.640 --> 29:05.082 My name is Irfan Muhammad, I work at Alexion AstraZeneca as a 65e87660-d3c6-4eb2-9110-da70e7119213-1 29:05.082 --> 29:08.080 part of the CMC drug development from pre clinical to 65e87660-d3c6-4eb2-9110-da70e7119213-2 29:08.080 --> 29:09.080 commercialization. b46c8ebe-68c7-4fd8-8aa7-cf8d255fee19-0 29:10.000 --> 29:11.080 Great presentation. 2ef44769-18cb-4325-8faf-007d9aed9c04-0 29:11.840 --> 29:16.403 Quick question, have you seen any challenges or any insights 2ef44769-18cb-4325-8faf-007d9aed9c04-1 29:16.403 --> 29:21.117 on outsourcing activities, how one could adopt A sponsor could 2ef44769-18cb-4325-8faf-007d9aed9c04-2 29:21.117 --> 29:25.381 adopt the transfer of data from the vendors to their own 2ef44769-18cb-4325-8faf-007d9aed9c04-3 29:25.381 --> 29:29.795 pipeline, So supporting let's say drug or drug release for 2ef44769-18cb-4325-8faf-007d9aed9c04-4 29:29.795 --> 29:32.039 clinical supplies and whatnot. a0fda016-ea47-4c64-b688-0114ea9f2836-0 29:32.040 --> 29:35.686 So development or analytical activities outsourced to the QC a0fda016-ea47-4c64-b688-0114ea9f2836-1 29:35.686 --> 29:39.572 lab and then they may have this integrated, you know, technology a0fda016-ea47-4c64-b688-0114ea9f2836-2 29:39.572 --> 29:43.160 or platform, but the sponsor may not have it or vice versa. e5a47b2d-dbd0-413c-a93b-6212b72d5038-0 29:44.240 --> 29:46.640 Inbound inbound data from vendor. 3c65d957-4bd8-49d5-ad0f-1cc5f67ba804-0 29:46.640 --> 29:47.400 Do you want to talk about it? 83bdbe9c-3910-416c-9d33-03947e5c4d0f-0 29:47.760 --> 29:51.223 So are you talking about inbound from a vendor to a third party 83bdbe9c-3910-416c-9d33-03947e5c4d0f-1 29:51.223 --> 29:51.440 lab? dbfe6f65-500f-4a69-9922-e894509c0ada-0 29:51.640 --> 29:52.320 Yeah, yes. f1b5506a-0e23-42a7-8be8-48e38c72d740-0 29:52.360 --> 29:54.720 And you're sending the samples to that lab for testing? 59f5f2f4-e75e-484a-8803-ceb564fa4097-0 29:54.800 --> 29:55.280 That's right. fa2db232-62df-4604-bb38-50aa27c35a7b-0 29:55.280 --> 29:57.680 And we had been waiting for their paperwork to be done. eb8697ec-91f1-4c70-bc46-ae214b98edb5-0 29:57.800 --> 30:01.076 And then we typically mirror what they're doing adds more to eb8697ec-91f1-4c70-bc46-ae214b98edb5-1 30:01.076 --> 30:01.560 the time. 7d8b5495-4e48-42c2-808d-d6c86fc87181-0 30:01.560 --> 30:02.040 Yeah, yeah. 01a584b6-1eca-4a40-bb4b-1b46145fbd1f-0 30:02.600 --> 30:03.440 And get the product out. 9738c841-af13-4f41-838a-0666de386fe4-0 30:04.000 --> 30:07.268 I, I think we're seeing, I mean we're seeing definitely more in 9738c841-af13-4f41-838a-0666de386fe4-1 30:07.268 --> 30:08.800 terms of how you transfer out. e2fad6df-0435-4cc5-b9f8-6c0f856c2a8a-0 30:09.120 --> 30:11.880 So for a third party lab to be able to do your testing. 5e60c2ac-8392-45b8-a3aa-af38c8d1c532-0 30:11.880 --> 30:15.438 So it really transfers externally the external to 5e60c2ac-8392-45b8-a3aa-af38c8d1c532-1 30:15.438 --> 30:19.779 internal flow of data just like the CMO space, right is it's 5e60c2ac-8392-45b8-a3aa-af38c8d1c532-2 30:19.779 --> 30:24.119 primarily manual, it's, you know, documents being exchanged. c5370af6-fb20-4013-95ef-2228eec0275b-0 30:24.480 --> 30:28.959 There is obviously advances in what is data exchanges for 3rd c5370af6-fb20-4013-95ef-2228eec0275b-1 30:28.959 --> 30:31.560 party manufacturers, but also labs. 9014b429-b5f1-46df-8a3c-adfeec0daeae-0 30:32.360 --> 30:36.038 And it really comes down to how much the labs are willing to 9014b429-b5f1-46df-8a3c-adfeec0daeae-1 30:36.038 --> 30:36.400 share. 5c88545a-d004-4234-8142-5ef4bf7f0b32-0 30:36.960 --> 30:40.219 And that's where I, I sometimes that becomes the sticky point in 5c88545a-d004-4234-8142-5ef4bf7f0b32-1 30:40.219 --> 30:43.227 terms of what they're willing to share and what they're not 5c88545a-d004-4234-8142-5ef4bf7f0b32-2 30:43.227 --> 30:44.080 willing to share. 1446e225-b75d-4214-b65f-c2ae00a1e1a4-0 30:44.320 --> 30:47.748 So I think this takes it back to how you set up your contracts 1446e225-b75d-4214-b65f-c2ae00a1e1a4-1 30:47.748 --> 30:50.905 and a lot of agreements are being revisited, but the, the 1446e225-b75d-4214-b65f-c2ae00a1e1a4-2 30:50.905 --> 30:54.170 challenge is in invisibility and, and what folks are being, 1446e225-b75d-4214-b65f-c2ae00a1e1a4-3 30:54.170 --> 30:57.762 are willing to share versus what they kind of, you know, tie up a 1446e225-b75d-4214-b65f-c2ae00a1e1a4-4 30:57.762 --> 31:00.319 report and I'm willing to send it over to you. f2efe45b-c111-4f75-b633-4e055e25ca39-0 31:01.160 --> 31:05.120 So definitely a very real kind of ongoing problem. 940c5a38-66ab-41dd-ab59-be926dcf63e9-0 31:05.120 --> 31:05.680 That's yeah, for sure. 3688ad46-ea4f-4b1c-8adc-92e82393083d-0 31:06.200 --> 31:06.560 Thank you. b57d2e8e-6a5d-4fc4-a1a4-00172f8f7bd2-0 31:06.840 --> 31:07.800 It's a whole new talk. 6316211e-d11b-4e40-8e3e-b27038d35220-0 31:08.200 --> 31:08.600 That's right. 736376d5-50cd-4696-88a2-9708c8b8d2ab-0 31:10.200 --> 31:10.880 Thank you, everybody. 93b584bf-896d-4674-a87a-87a78a80c783-0 31:11.000 --> 31:11.360 Thank you. fc81261f-5428-48b2-a6f6-92e2dd3fcdcf-0 31:11.680 --> 31:12.080 Thank you.