These 10 Basic Data Analytics Techniques Could Accelerate Drug Discovery.

Pharmaceutical companies have researched and made observations for centuries to understand treatment's efficacy better. With the rise of technology, data analytics in pharma has become a valuable tool for leveraging essential information and detecting patterns. Information technology has revolutionised the way we process and access data. 

Through advancements like Big Data, organisations can now analyse more significant volumes, variety, and velocity of information than ever before. This opens up huge opportunities in industries such as drug discovery or healthcare; McKinsey estimates that a successful Big Data strategy could bring an extra $100 billion annually in value to the US healthcare system alone. 

Companies should take advantage of such possibilities and develop strategies for tapping into this potential. Big Data and data science offer tremendous promise for those looking to unlock their value. Generating business value and driving innovation within the pharmaceutical industry requires a fresh outlook on data. Here are a few techniques of data analytics in pharma that can be put to work in drug discovery.

Make Data Science A Core Component Of Drug Discovery

Data science is the discipline at the intersection of statistics, computer science, and drug discovery. It's essential for the pharmaceutical industry in terms of competitive advantage because it can extract fundamental knowledge from public and proprietary data. Drug discovery activities have been around for decades, but data science has only recently developed as part of it. Although many roles are associated with this field, such as clinical statisticians, biostatisticians, computational chemists, and biologists who use in silico analyses to aid development--data science has become even more popular since its emergence.

Recently, machine learning engineers and data scientists specializing in deep learning, image processing, and body sensor analysis have become increasingly popular within pharmaceutical companies. As an organization, they recognize the positive impact these scientists can have on early and late drug discovery analytics projects. However, senior leadership teams must shift to reflect this evolution in technology and science to embrace data science in their drug discovery processes fully. This means observing leadership changes and cultivating dedicated project teams trained in data science methods.

For successful drug discovery analytics and development within the pharmaceutical industry, leadership teams must possess excellent data science knowledge and its potential, applications, boundaries, and risks. Data science leaders need to be better integrated into the organization to bring data science into decision-making bodies and foster awareness among departments for computational approaches. While relatively new compared to other roles in drug research, data scientists bring fresh perspectives with their various skill sets and creative problem-solving abilities that can provide valuable insight.

The FAIR Data Principles

Data generation is expensive and intensive, incentivizing organizations to utilize existing datasets for various purposes. However, the data can be easily formatted and documented correctly. This is especially true for large companies that have accumulated large amounts of data over the years without proper curation. 

The FAIR principles provide guidelines to help make the existing data more "Findable," "Accessible," "Interoperable," and "Reusable"; however, retroactively implementing these rigorous standards can be highly cost and time consuming. Implementing FAIR play processes along the entire data lifecycle is essential for optimizing the quality and utility of generated datasets. Adequate data and metadata management strategies must be executed from the point of data generation.

Integrate A Data Store With Analytics And Visualization

Data-driven insights are crucial for achieving a competitive edge. Novartis launched the Nerve Live and data42 projects to facilitate data discovery techniques, connection, and analysis. As more pharmaceutical companies recognize this benefit, they have begun investing in similar endeavors to harness the power of knowledge discovery from data. 

Adhering to FAIR principles is necessary for this challenge, but more is needed to unlock the data's value fully. To develop resources and tools that can be used by everyone from data scientists to clinicians and even experimentalists, there needs to be a central search engine indexing all data as well as knowledge of key entities and relationships among them like targets, compounds, indications, biological pathways, experiments, and portfolio projects. 

Furthermore, an interactive graphical user interface should also be included to simplify the exploration and visualization of datasets. Finally, strategic investments into data management techniques like repository formation, metadata documentation, and systemic FAIR play processes must be considered when leveraging these tools to reach their full potential.

To effectively meet the needs of any large organization, they need to maintain a flexible system for data management. It should offer specialized dashboards and reports that cater to experimental scientists and clinicians and enable programmatic access through scripting languages such as R, Python, or SAS. R might have an edge in the pharmaceutical industry due to its popularity with the bioinformatics community via Bioconductor. Similarly, Python has become increasingly favored in recent years, and clinical statisticians may prefer SAS. Graphical interfaces such as Shiny, Spotfire, and Tableau can provide further accessibility options.

R and Python can be beneficial when constructing an efficient ecosystem for data wrangling, modeling, and visualization. Both have a variety of packages that offer excellent solutions - the tidyverse in R and pandas, scikit-learn and matplotlib in Python. However, it's essential to understand what kind of data is being used when integrating these tools with other domain-specific solutions. The goal should always be to maintain best practices and ensure reproducibility while accounting for different organizational needs. All of this is vital for successfully designing and implementing a centralized analytics system.

Create A Strong Community For Distributed Data Science Teams

Pharmaceutical companies have responded to collaboration challenges between data scientists and their counterparts within organizations by experimenting with different models for success over the past two decades. To make the most effective use of data science teams and optimize their potential, it's necessary to design a model fully fit to meet the needs of large pharma enterprises, one in which size won't become an obstacle.

The classical model is a good solution since it provides ample flexibility and resourcefulness by providing central data science support to tech/platform, disease, and program departments. This way, it's easy to shift focus when priorities change quickly within the company while still meeting project deadlines.

To ensure the maximum utility of biomedical data across the organization, distributed computational teams should be incorporated within each department. This supports continual engagement with projects within a predefined domain (e.g., target discovery and drug safety) while allowing data scientists to refine their skills and further understand particular aspects of drug discovery. 

Make The Organization More Digitally Savvy

Drug discovery Analytics is a complex process that requires collaboration between data scientists, experimental scientists, and clinicians. To ensure success in their digital transformation initiatives, companies must ensure their teams are familiar with the biological details of drug discovery techniques and the basic mechanics of data science. Companies should invest in training programs to equip all staff members with the knowledge and skills to effectively use novel data science technologies and contextualize their datasets within external sets.

Any successful scientific collaboration needs an efficient and cooperative environment. To this end, it is essential for computational and experimental scientists to both understand each other's disciplines to unlock their potential. This understanding will help both parties agree on best practices for experiment design, allowing data scientists to develop a question-based approach to analyzing data. These measures aim to remove communication barriers between the two fields and create hybrid scientists adept at using data to bridge the gaps.

Take Advantage Of Artificial Intelligence Without Overhyping It

The biomedical industry is currently undergoing a transformation driven by two significant aspects: the generation of large amounts of data and more advanced machine learning methods. Deep neural networks fall into this umbrella and play an increasingly important role in data-driven applications in healthcare-related fields. 

In the past, biostatisticians, chemists, and computational biologists have been utilizing ML techniques for years. However, with advances in AI technology, media coverage has significantly raised the profession's profile. Data science is now widely recognized within both expert and general circles alike - leading to unprecedented demand that risks causing an oversell of capabilities in AI applications.

The pharmaceutical industry is significantly impacted by the emergence of machine learning and AI approaches, from disease understanding to patient classification. While these tools are potent, there are still some limitations that must be acknowledged. Finding large, appropriately annotated datasets and contextualizing the results remain challenging tasks. 

Machine learning should only be seen as a universal fix to some problems in the pharmaceutical industry. Instead, it should be considered one of several valuable tools in our data science arsenal that can help us understand mechanisms and promote real impact.

Integrate Strategic Partnerships With Internal Capabilities

Pharmaceutical organizations must develop an organizational structure that combines internal capabilities with external opportunities, such as building a collaborative ecosystem across the industry, technology providers, and academic centers. 

To take advantage of innovative advances outside of the institution, data scientists should employ a fast-follower approach; this allows them to quickly apply methods developed by researchers in academia and adapt these solutions for their use. This strategy can rapidly implement scientific progress on drug discovery techniques while leveraging established tools and resources.

Pharmaceutical companies need to use reproducible data solutions, uniform methodology, and consistency with external best practices. Free, open-source software reduces the risk of inaccessible or unusable data due to generating closed-source or restrictively licensed software. It also aligns with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. 

Furthermore, the industry should give back to the data science community by making valuable data, tools, and benchmarks available. Drug discovery process and development often use free, open-source software – let's ensure this trend continues.

Ensure That Data Science Teams Have Adequate And Appropriate Resources

Data science initiatives can bring a significant return on investment, as automation of computational pipelines and code reuse can significantly reduce turnaround times. However, care must ensure the analysis is tailored to fit the project requirements and deliver real impact. Therefore, data science teams should be resourced appropriately; according to our estimations, research departments should employ at least 10% of data scientists to maximize speed and effectiveness. 

Different departments may require a different ratio of computational-to-experimental scientists depending on their needs; for example, tech groups will likely need more original computing power than the disease or product focused teams.

It's essential to understand the difference between roles and data specialists. Data engineers build, manage and maintain data systems; data stewards organize the data and metadata into formats that enable cross-experimentation; data wranglers process raw data and integrate different sources of information; finally, the data analyst works with scientists, chemists and clinicians to answer scientific challenges. 

While a 'data scientist' may be able to cover many of these roles, everyone has an area of expertise in which they specialize. This design ensures talented members can contribute in their areas most effectively and bring efficiency and insight into larger systems.

Organizations must be mindful of investing in digital resources to foster successful data science teams. With adequate financial backing, data scientists can deliver their fullest potential due to a lack of required datasets, software licenses, external collaborations, professional services, and hardware. 

Therefore, appropriate resourcing should be allocated for digital transformation within the pharmaceutical industry. It is paramount for providing drug discovery teams with the necessary tools and technology inclusion alongside progressing unstructured datasets among departments and taking advantage of available external data that could positively impact projects.

Engage In Talent Recruitment And Retention

Data scientists with domain knowledge, scientific story-telling abilities, and computational modeling skills are essential for any project to succeed in the pharmaceutical industry that involves extracting actionable insights from data. Data scientists are in high demand across sectors like academia, consulting, financial services, and technology firms, particularly as digitalization revolutionizes healthcare. Technology companies are increasingly investing in health divisions and looking specifically for those with biomedical expertise.

Biomedical innovation in the coming decades demands that pharmaceutical companies recruit a qualified and experienced data science workforce. Due to a limited supply of data scientists, traditional hiring systems based on staffing expectations for experimental scientists are no longer practical. 

Pharmaceutical organizations must adjust their recruitment procedures to reflect the higher demand for data scientists on the international job market and rising salaries among other competitive industries like tech and finance. Companies can ensure they stay ahead of the curve by keeping up with global industry trends and benchmarks for recruiting for the data scientist role.

Data scientists need to have chances to specialize and progress in their careers. There should be talent transfers between divisions which will permit data professionals to expand their reach and stay within the company. Of course, salary, benefits, and promotions are all necessary incentives for optimizing talent; nevertheless, intrinsic motivations are equally important, if not more so. Leaders must create an environment where people may generate ideas independently, focus on mastering skills and find compelling goals beyond usual data analysis tasks.

High-Quality Data Generation

High-quality data is indispensable when it comes to drug discovery. Regardless of how sophisticated, data analysis can only provide meaningful insights if it's done on low-quality data or experiments with flawed designs. Unfortunately, data analysis is too often seen as an afterthought that can be plugged in at the end of the investigation. 

Poorly designed experiments will give limited biomedical information and take longer to analyze, straining data science resources that could be used for other projects. Experimental designs should apply best practices and account for data analysis needs to maximize accuracy and increase the interpretability of results in a timely manner.

To maximize the advantages of data science, successful organizations put equal emphasis on both data generation and analysis. Collaboration between data scientists and experimentalists should start early; they must ensure that they understand each other's needs, objectives, and vision which can be achieved through communication, discussion, and an exchange of information. 

Additionally, these organizations recognize data as an essential asset and approach novel technologies, academic collaborations, and business partnerships with a unified plan for managing and processing the collected data. Proper integration also promotes accountability among various teams, leading to improved drug discovery results.

Conclusion

Data analytics pharmaceutical have the potential to drive rapid advances in the drug discovery process. In this article, we provided an overview of ten basic techniques that can be used to extract valuable insights from data. As new technologies emerge and analytics capabilities expand, it will become increasingly important for pharmaceutical companies to stay abreast of these developments by investing in solid data analytics teams and keeping training and knowledge exchange up-to-date.