How to create an efficient localization ecosystem for life-science companies

Due to the nature of the industry itself, regulatory compliance emerges as one of the biggest challenges, if not the biggest, regarding localization in the life-science industry. Regulation-related challenges come in many different forms: from adhering to tight time frames to ensuring that there is an audit trail for all translated content and complying with SOPs, life-science enterprises need to ensure that they meticulously follow every guideline and requirement set forth by regulatory bodies to maintain the integrity, accuracy, and safety of their products and services across global markets. Let’s break them down.

Life science companies face strict regulations in all their activities. Compliance is crucial, leading to numerous challenges, with few as significant as meeting stipulated time frames. For example, the new EU-CTR (European Union Clinical Trials Regulation) which came into force in January 2023, states that all clinical trial submissions must include documentation translated into all the languages of the trial participants. But the main challenge comes from having to respond to all requests for information in all of the languages within the space of 12 days – otherwise the application may be automatically withdrawn.

To be able to meet these requirements, life-science enterprises must be equipped with the right team of professionals to do it, but perhaps more crucially with the technology that enables them to carry out their work with as few delays as possible to meet the deadlines. The consequences of not meeting them can result in huge revenue losses for the company, as well as loss of competitive advantage and brand image in certain markets.

Delivering all multilingual content on time means nothing if the quality of the translation is not excellent. In other words, it’s not enough to just submit it through machine translation, but rather it must be post-edited by the necessary in-country reviewers (ICRs) and subject-matter experts (SME), to ensure that not only the terminology is accurate but that it is also understandable in the target language, making the process more of transcreation rather than simple translation. 

For this, linguists and reviewers must be equipped with technology that enables them to perform these checks with all the information needed at hand, without incurring too many delays. The translation solutions chosen by life-science enterprises, therefore, must be equipped with robust terminology tools, customizable quality-assurance (QA) profiles, and reliable audit trails to track all changes made and ensure that there is full traceability of the process from start to finish to comply with internal SOPs and regulatory requirements. A wrong decimal here or there (e.g. a dosage of 1.32 instead of 13.2) or an incorrect translation or use of a term (sitotoxin vs cytotoxin, two terms that have nothing to do with each other) can have disastrous consequences.

Most life-science enterprises require the translation of millions of words every year into multiple languages, which only multiplies that initial number exponentially. Having this volume translated by humans within the stipulated time frames is unrealistic as it would be too costly and extremely time-consuming, which is why machine-translation engines become an essential tool in the localization process.

Being able to translate these high volumes therefore requires, once again, being equipped with the right technology that enables this to happen. When it comes to machine-translation engines, some are better trained than others in certain domains or languages, so having a deep understanding of which are better suited (which can be achieved by consulting internal or external localization experts) can save valuable time down the line by reducing the time of post-editing steps.

Automation is just as key. In workflows requiring multiple stakeholders’ involvement, offline communication and notifications, such as emails or sending files, are not an option when the time frames are so small. Therefore, automated workflows within a single solution are vital to move the project from one step to the next without incurring delay while also minimizing the risk of human error.

But what are the components of an efficient localization technology stack?

The first step in maximizing your translation efficiency is separating the different content types and understanding their priorities. For example, the level of validation required for the content of a clinical trial compared to internal communications is the complete opposite, therefore applying the same level of scrutiny to all translated content is simply not efficient.

This can be managed via a TMS by deploying different project templates for each content type and applying different workflow steps and tools per template depending on their nature. For example, you can have a specific template for content that does not require validation, such as internal communications that won’t be seen by the end-user customer. These templates should not necessarily include specialized stakeholders, such as SMEs or ICRs, as their time would be better spent working on valuable content. For sensitive content whose quality must be 100% accurate, you can create templates that include the SMEs and ICRs in the workflows. Creating different templates is not just a huge time-saver but also makes a big difference in allowing each team member to focus on the tasks that add value.

Another activity that can enhance efficiency is choosing the right machine-translation engine for each task. Not all machine-translation engines have the same capabilities: some are particularly useful for certain language pairings, and others for specific models or tones. Knowing the strengths and weaknesses of each machine-translation engine can drive efficiency by saving precious post-editing time during reviews. As mentioned above, you can find out the strengths and weaknesses of each via internal or external linguistic experts, or by asking your tech providers.

The third and last point is ensuring that non-linguistic stakeholders have it as easy as possible when accessing the localization tools they need to send translation requests to the right teams or perform reviews. It may be impractical to grant access to localization technology to thousands of users, never mind train them in it, so in this case connectivity becomes key. By being able to connect their original platform to the TMS, they can operate directly from it and send requests or comments from their original platform. For non-linguistic users who need to review, a TMS with a simplified view (i.e. without all the details like fuzzy matches, inline tags, or concordance searches filling up the screen) goes a long way in allowing them to focus solely on the content itself so that they can just read, edit, and submit.