The pharmaceutical industry is mired in regulatory compliance requirements, which means businesses need to have well-established pharmaceutical R&D data harmonisation standards. Any omission, inaccuracies, inconsistencies or errors in data recording and reporting can carry a lot of risk, which means pharmaceutical R&D data harmonisation companies must be able to provide a complete audit trail of each step within every process – from the development of drugs or products to bringing them to the market to tracking them throughout their lifespan.
In the pharma sector, every tweak, change or improvement to a product or the way it is distributed in the target market or geographical area must be fully verifiable and validated – in terms of documentation and labour alone, it’s a huge undertaking. Vigilance is everything for one wrong move, such as incomplete advice, inaccurate translations for new markets, mislabeling, or incorrect dosage, can translate into catastrophic fines for the business and irrecoverable reputational damage in most cases, not to mention the risk to their consumers’ health.
It is, therefore, not surprising to know that data forms the backbone of pharmaceutical research and development (R&D). From uncovering novel insights on a new drug to driving more informed decision-making and strategic planning – data fuels the progressive engine of all R&D endeavours.
While most pharma companies work with massive volumes of diverse data generated from multiple sources across the various stages of R&D, many of them find it difficult to deal with the significant challenges involved in processing, storing, and updating that data.
This is where a customisable and comprehensive pharmaceutical R&D data harmonisation system is required so that a strategic approach to integrating, standardising, and optimising disparate datasets can be employed across all stages of the R&D pipeline. Not only will it make businesses in the pharmaceutical sector more efficient and compliant, but ultimately, more profitable and competitive.
Data harmonisation is a process where data from different, disparate sources is standardised to make it compatible and suitable for analysis. This, in turn, can help fuel the R&D process, with the possibility of exploring new concepts, ideas, and technologies or to explore and validate ideas, for example. It can often uncover hidden insights which businesses can use to improve efficiencies, cut costs, and become more competitive.
Early stage R&D can especially benefit from a tried-and-tested pharmaceutical R&D data harmonisation model as it typically involves bulky data which can arise from all the various experiments being performed in-house or acquired through public data repositories, for example.
Harmonisation of such datasets entails completing missing annotations and establishing uniform data structures for the purpose of acquiring high-quality data – which can then accelerate downstream analyses, and more.
This process is, in fact, critical in facilitating pharmaceutical research into disease mechanisms, prospective drug development and targets, and discovering new therapeutic approaches.
A pharmaceutical R&D data harmonisation system can offer businesses a myriad of benefits which can help drive innovation forward and enhance the pace at which projects are delivered. Perhaps, one of the most prominent benefits that come to mind is the ability to accelerate R&D timelines. Since data integration and standardisation processes are streamlined by employing pharmaceutical R&D data harmonisation standards, the core data harmonisation process can greatly minimise the time it takes for data pre-processing – a very important stage of the harmonisation process – and frees up plenty of time which can be allocated to analysis, research, and insights.
Pharmaceutical R&D data harmonisation companies which have already integrated a robust harmonisation process at the core level are reaping numerous benefits, which have all proven to be instrumental in optimising analysis workflows, streamlining data integration, and maximising the utility of diverse datasets, for example.
Let’s discuss five specific benefits of using a pharmaceutical R&D data harmonisation tool:
With a pharmaceutical R&D data harmonisation model in place, your data harmonisation efforts will always lead to improved consistency across diverse datasets, as all the structures, formats, and metadata will be standardised.
When you enforce uniform data conventions and semantics, for example, data discrepancies and inconsistencies can be cut down by a wide margin, thus, leading to higher quality data which is then ready to be processed by machine learning (ML) algorithms – these can be used to achieve specific business goals and drive real patient outcomes.
Furthermore, standardised data will always facilitate more accurate comparisons, reduce analysis-related errors, and enhance the overall reliability of research outcomes.
Data for pharmaceutical R&D typically comes from diverse sources which include both proprietary data generated within the organisation and data obtained from public repositories. Naturally, the various datasets differ in structures, formats, and metadata completeness.
A pharmaceutical R&D data harmonisation system allows for smooth integration across all data sources. When you have integrated data at your fingertips, you essentially have more statistical power to conduct analysis and gain the required insights faster, as your analysis pipelines and algorithms will be ready to do their work.
Another key benefit of having a pharmaceutical R&D data harmonisation process in place is to be able to facilitate better interoperability and integration across contrasting or disparate datasets.
When you align the data from different sources with a common framework, everyone can easily access it through seamless data exchange and integration – and this is only possible through a powerful yet easy-to-use pharmaceutical R&D data harmonisation tool like Harmony.
With this interoperability in your hands, you can foster improved collaboration among researchers, promote better knowledge sharing, and facilitate enhanced cross-functional analysis. Ultimately, this will lead to a deeper, more comprehensive understanding of what your researching – e.g. a complex biological phenomena.
When data is harmonised properly, it streamlines and simplifies workflows for data analysis. In turn, this reduces the time, effort, and resources needed for both data processing and interpretation.
With standardised structures and formats, your researchers can automate data manipulation tasks, utilise analytical tools more efficiently, and focus on extracting meaningful insights instead of constantly playing a tug of war with data inconsistencies.
With a streamlined workflow, thanks to your pharmaceutical R&D data harmonisation model, you can accelerate research timelines and enhance productivity across all data-driven objectives.
A pharmaceutical R&D data harmonisation model bespoke to your company or organisation can help you unlock the full potential of datasets, thus, improving both data accessibility and exploration.
When you are able to consolidate and standardise diverse datasets, your in-house researchers can access a broader spectrum of data, essentially allowing them to perform more comprehensive analysis and exploration.
This, in turn, will facilitate the identification of hidden trends and patterns as well as correlations within the data which can lead to novel discoveries – along with the prioritisation of potential insights which may not have been discoverable while working with fragmented datasets.
Ultimately, a pharmaceutical R&D data harmonisation system can empower your organisation to remove redundancies within your data processing workflows. What’s more, existing datasets can be put through faster timelines and fully leveraged for research breakthroughs and progress.
Finally, with a tailored pharmaceutical R&D data harmonisation process in place, regulatory compliance matters can be better dealt with, ensuring more rapid passage through regulatory red tape, thus, expediting research discovery as well as application.
While having a quality pharmaceutical R&D data harmonisation tool in your arsenal from the outset is important, the ability to produce reliable, high-quality, and reproducible data is just as important, no matter what kind pharmaceutical R&D data harmonisation companies set out to do.
However, incorrect research and development data where data quality has not been kept intact can severely impact the outcome of the project, which is why pharma companies of all scales must establish a data quality culture throughout their entire value chain.
Here are five actions pharmaceutical R&D data harmonisation companies can take to improve the effectiveness of not only their research but especially their harmonisation processes.
Putting emphasis on a risk-based approach is especially important when it comes to research and development in the pharmaceutical sector. Why? It ensures optimal data quality and puts you in an advantageous position to mitigate potential risks as you determine the criticality of your data and its underlying impact.
Moreover, adopting risk-based approaches and methodologies enables you to balance your resources with your business needs and process burden more effectively, thus, maximising assessment impact. One of these risk-based approaches is critical thinking which involves the systemic identification, assessment, and addressing of risks throughout all stages of drug development, and not just R&D.
These risk-based approaches or principles are also in-line with the WHO’s “good practices for R&D facilities of pharmaceutical products”, and pharmaceutical R&D data harmonisation companies are increasingly applying them at an early stage of research.
When you implement a risk-based approach within the R&D domain in pharma, you can enhance product safety, optimise resource allocation, and improve the decision making process.
Data integrity is needed to help come up with the characteristics for building and preserving confidence in the reliability of your data throughout its lifecycle.
And, throughout this lifecycle, the data must not be altered in an unauthorised manner. The principles governing data integrity can be read up on in detail in the ALCOA+ concept. Many pharma and life science companies rely on the ALCOA+ framework to establish data integrity in their research. It can be said that it’s become the gold standard established by regulators for meeting and maintaining compliance with all data integrity regulations.
In pharmaceutical research and development, defining expectations and committing to a minimum set of data characteristics (which includes metadata), points to their significance as ‘valuable resources’. In addition, this ensures an adequate level of data integrity, because based on that data, scientific results will be drawn, so a data integrity framework like ALCOA+ has proven to be an extremely important resource for pharma companies, no matter how big or small their R&D projects.
Data integrity, as described in the ALCOA+ principle, focuses on human-to-human document-based interaction, such as summary reports and protocols, for example. However, complete data integrity is not achieved by only adhering to the ALCOA+ principles but by striving for a strong data culture within your organisation.
A prerequisite for developing this kind of culture is to integrate and maintain a data governance programme – i.e. clear rules for data management and stewardship which unlocks an organisation’s full potential toward useful, impactful, and high-quality data. Additionally, data ownership and responsibilities must be defined as well.
A pharma organisation should be flexible and agile enough to shift from the document-based ALCOA+ principle to a more data-centric one when needed, such as the FAIR principle.
The FAIR principle essentially makes it easier to make the data Findable, Accessible, Interoperable, and Reusable within a business, unlocking the immense value which data brings, and driving business performance. No more scattered or siloed data sets, where the value and efficiency of data is maximised.
Ultimately, organisations must learn to mesh a solid data culture with meaningful data governance, thereby improving all the key processes involved in data analysis. When document-focused principles are complemented by data-focused compliance methodologies like FAIR, it can help organisations unlock the true value of data, improving workflows in both drug discovery and research & development.
The underlying quality of the research system plays a pivotal role in overall R&D activities as it ensures that the necessary standards and requirements are being met.
Your quality system should include the following vital components at a minimum:
Additionally, the quality system must cover when data is to be submitted for approval in marketing authorisation applications. All the associated batch data, results, and related information must meet the defined standards.
The documentation of analytical procedures or functions developed by the R&D facilities need to be meticulous in order to successfully transfer them when needed. Finally, the implementation of a robust research quality system will help pharma organisations maintain data integrity, with the added benefit of regulatory compliance and reliability of R&D efforts being upheld.
Cultivating a quality mindset when moving forward in pharmaceutical R&D data harmonisation standards is downright essential. Promoting a quality culture revolves around raising awareness, and providing the necessary onboarding and training.
Additionally, scientists engaged in the harmonisation process must be provided with a reasonable amount of mentoring opportunities. A strong quality culture is typically undertaken by senior management, as specified in multiple guidelines, including the Data Integrity PIC/S Guidance.
This quality culture ought to reflect in an open and transparent professional environment where individuals within the organisation are empowered by encouraging a sense of ownership as well as responsibility to maintain high-quality pharmaceutical R&D data harmonisation standards.
Senior management must foster a culture of positive error as it can promote the detection of errors early in the R&D lifecycle. Mistakes will, therefore, always be seen as opportunities to learn and improve. When employees are taught to seek out errors instead of trying to conceal them out of fear of being penalised, they will feel empowered. Furthermore, by offering incentives for excellence and quality adherence, your research team will always prioritise quality in their work.
By embracing all the above practices and principles, the pharmaceutical R&D data harmonisation process can be carried out by fostering a ‘quality-first’ mindset among the research team. This will ensure the safe and effective development of medicines or drugs which the end user can truly benefit from.
Having a tried-and-tested pharmaceutical R&D data harmonisation tool at your disposal will certainly help to speed things up and possibly give you a competitive edge. However, it still pays to understand some of the challenges involved in performing R&D data harmonisation:
Ensuring the quality of you data and its completeness are vital to making your data harmonisation efforts count. However, as you might already know, heterogeneous datasets can vary in terms of reliability, completeness, and data quality – this poses challenges when it comes to the integration and reconciliation of disparate datasets.
Let’s take public repositories – these hold data with missing annotations and essential metadata, both of which are necessary for data accessibility and applicability. Incomplete or missing metadata tend to introduce long and undue delays in research timelines because, unsurprisingly, addressing them can be a very taxing process.
In addition, dealing with data quality issues requires extensive data validation & quality control measures; this includes data cleansing and normalisation as well as validation, ensuring that your harmonised datasets boast high integrity and reliability.
The pharmaceutical R&D data harmonisation process must address huge data volumes which are routinely generated as part of day-to-day research and development. Through the course of handling bulky data files which tend to have many missing values, merging experimental data with clinical data into one file, and meeting diverse input as well as output criteria – are just some of the issues which can arise due to handling such data.
To deal with these issues effectively, a robust data infrastructure is needed, in addition to abundant computational resources. While certain analytical solutions can be effective for just one small dataset, they tend to fail where the task requires processing several terabytes of data which resides in typical datasets. Plus, effective scalability demands robust and large-scale data infrastructure as well as computational expertise.
Fully harmonised datasets can present a fair amount of challenges in data analysis, and that’s particularly because of how complex the data can be. Analysing integrated datasets, for example, demands the use of advanced techniques such as domain expertise, computational resources, and analytical pipelines, to help navigate and interpret the data in an effective way.
Choosing the appropriate analytical methods and algorithms is actually a vital piece of the puzzle as it makes for effective downstream analysis, something which is a critical step in research. Also, the integration of data from multiple sources can introduce confounding factors, biases, and technical challenges, all of which can make data analysis workflows complicated and cumbersome – requiring researchers to carefully consider and validate analytical results.
Research in the pharmaceutical sector encompasses a broad range of experiment techniques, data types, and platforms, so investigators and researchers are always working with highly diverse data sources. This means that they must contend with the heterogeneous datasets being generating from multiple sources, including in-house data and public repositories.
The harmonisation of these diverse datasets requires consolidating differences in data structures and formats as well as semantics, making it challenging to carry out the data integration and standardisation process.
A major challenge in pharmaceutical R&D data harmonisation arises due to the inconsistencies in data standards, formats, and metadata schemas. Different research groups and organisations tend to adopt their own conventions for representing data, which makes the reconciliation and standardisation of disparate datasets difficult.
Within large multi-disciplinary teams and research organisations, data silos often pose barriers to knowledge sharing and collaboration. Fragmentation can occur across the various departments and platforms as well as private and public repositories, which can further complicate things.
To make the harmonisation and data integration process smooth, robust data management practices must be incorporated which will certainly help to overcome data silos and fragmentation.
In closing, you should always work with a versatile and customisable pharmaceutical R&D data harmonisation model when carrying out any kind of pharma research and development. The Harmony app boasts a powerful harmonisation engine which can process data with ease, transforming all your datasets into a consistent data schema. It helps you ensure uniformity across all your structures, data formats, semantics, and more – greatly boosting the efficiency of your R&D efforts from start to finish.
