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What works, and what doesn’t? Life science enterprise laboratory informatics provider Benchling also notes that companies looking to either initiate or update their digital transformation journey should start by taking stock of their existing infrastructure. Michael Schwartz, head of product marketing, said: ‘Organisations must be honest about what’s working and what isn’t, and also about what they want to achieve, rather than focus on specific categories of software. It really has to do with their workflows and R&D goals.’ Complexity aside, companies are


still concerned about some of the fundamentals, such as security. Every organisation is in a different spot in their transition into the cloud, Schwartz noted. ‘Most companies will still need assurance that their data will be as secure, if not more, in the cloud than it is on-premise.’ Connectivity is another key consideration, according to Schwartz, and the ability to integrate platforms from different vendors comes high on the list of requirements. ‘Organisations are aware that they will,


if they don’t already, implement multiple platforms across their organisation – not just in their R&D.’ So while their existing processes may


have little connectivity, that ability to integrate different platforms – whether those already in place or those yet to be purchased – is also high on the list of digital requirements. ‘Organisations know that if their data doesn’t aggregate in a very clean and structured manner, they are going to have challenges. They want to make sure that they have that connectivity, so that they can integrate across applications and data repositories.’


Outdated platforms A willingness to look at existing software objectively will highlight problem areas with possibly outdated platforms, Schwartz noted. ‘Early generation ELNs, for example, offered a fairly narrow field of data capture. They were designed primarily to capture unstructured data, and to document intellectual property.’ But the software was not well connected as part of a holistic R&D operation. In fact, such platforms were not much


different than a paper notebook, he suggested. ‘Individual scientists received access to an ELN for instance, but there was no connectivity, so any experimental data that was put in the ELN wasn’t readily accessible for sharing and scientific decision support. ‘That’s not the way science works now.


There may be multiple specialised teams all working around one common endpoint,


20 Scientific Computing World Autumn 2020


experimental design in the same location, and how their scientists can achieve individual productivity gains. ‘Then companies typically want to


address operational inefficiencies, which usually involves rethinking how they standardise their database of biological and chemical entities, and how they co- ordinate requests and workflows. ‘Lastly, companies will then seek to


“It often starts with a need to document molecular and experimental design in the same location, and how their scientists can achieve individual productivity gains”


and they need to organise around data generated by multiple individuals and different teams to drive them to that endpoint.’


Flexibility and connectivity Since Benchling was founded eight years ago, Schwartz said it has developed a flexible, unified, cloud-based informatics solution for life science R&D organisations, ranging from the largest enterprise pharma companies to the most cutting- edge biotech startups. ‘Our modern approach and scientific


expertise has allowed us to build a highly flexible platform which can be easily configured to support a diverse set of R&D functions in this space, which are all linked through a connected platform. We can now address some of the mission-critical pain points in life science R&D. Individual organisations all have their own distinct challenges’, Schwartz said. ‘These may centre on managing


inventory, or on tracking samples, or on centralising and standardising data capture, but the important thing is that we can address all of these different use cases on one unified platform.’ Companies in the biotech and pharma


industry are at various points on their digital transformation journey, Schwartz stated. ‘In life sciences, it often starts with the need to document molecular and


drive smarter program-level decision making, which involves aggregating their data for analysis and visualisation. Often these different functions are addressed holistically at the same time, but it’s possible to solve for these acute needs in phases, as part of a broader transformation process.’ Benchling believes two key factors set it


apart from some other platform providers in the same field. The first is that all the implementation teams that work with clients have deep domain expertise in the life sciences, enterprise software, and data modelling, so they understand the scientific needs of biotech and pharma organisations, as well as the IT and business requirements. ‘Whether an organisation is focused on small molecule R&D, or is involved in the biologics arena, they will work with a Benchling team that has carried out multiple implementations in their area of specialisation, from traditional chemical drug discovery, to vaccine research, gene therapy or synthetic biology and industrial applications,’ said Schwartz. The second hallmark of the Benchling


solutions is that the platform can be implemented without code. This makes the platform fast to configure and easy to deploy. ‘We run an agile implementation process, and iterate very quickly with our customers, so we can get a prototype up and running quickly. This can then be fine- tuned so that customers get maximum benefit, without complex customisation processes.’ The company has developed a mature


set of Rest APIs, so connecting the Benchling platform to existing systems should not be problematic. ‘But we also have partnerships with instrument vendors, and have developed templates for common laboratory instrumentation, so most clients can get up and running, and connect with almost any instrument very quickly,’ said Schwartz. ‘As the aim is to automate those manual


processes as much as we can, we have a lab automation module that sends run instructions to robotic liquid handlers and analytical instrumentation, then digests the output data in a structured, actionable format.’


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