INTRODUCTION
Adopt AI-driven automation to speed up drug discovery
Since the start of the COVID-19 pandemic, life science and pharmaceutical manufacturers have rapidly embraced automation to drive digital transformation along every touch point in the pre-clinical and clinical trial processes. Many applications have come up that promise to accelerate drug and vaccine research and development. Digital transformation is fueling innovation at the world’s most successful healthcare, medical, life science, and pharmaceutical enterprises.
Implementing data-driven decision-making to speed up drug discovery and pre-clinical and clinical trials is the obvious choice of many AI-intensive pharmaceutical companies that witnessed the development of the COVID-19 vaccine in just ten months.
In 2021, 97% of clinical trial sites used at least one form of remote technology. Over 89% of sponsors embraced decentralized clinical trial technologies, and 88% of trial sites used some form of electronic consent services. Other critical applications, such as eISF, eTMF, eCOA, ePRO, CTMS, and electronic source data software,are used as tools to improve efficiency, accuracy, and transparency in the development of new treatments.
AUTOMATION
Why is automation crucial for clinical trials in healthcare?
Drug discovery is a slow process that extends to over a decade. From initiating the drug discovery to when government agencies grant market approval, R&D units spend 10-12 years.
Half of this period is devoted mainly to pre-clinical and clinical trials. Artificial intelligence(AI) and machine learning (ML) can reduce the time taken for some of the processes. AI and ML enhance the ability to work with extensive and complex data sets through automated processes.
Health data science is evolving rapidly as custom AI and ML software applications deliver the efficiency needed to improve pre-clinical and clinical trials dramatically.
The global automation and artificial intelligence in the healthcare market reached $14.6 billion in 2023 and is expected to grow to more than $102.7 billion by 2028—with a compound annual growth rate (CAGR) of 47.6% anticipated during the forecast period.
ML allows medical researchers to work with larger and larger datasets to interpret details about patient populations to develop life-saving medical interventions.
This blog explains why bio-science and pharmaceutical enterprises implement automation to improve their pre-clinical and clinical trial technologies.
BENEFITS
Implement automation for accuracy in clinical trials
AI-enabled automation is transforming how medical researchers analyze and develop new molecules in many exciting ways.
- A subset of ML, deep learning, uses neural networks to improve the validity, reliability, and precision of data analysis and interpretation.
- Intelligent bots improve technical processes by automating repetitive tasks to yield better data accuracy and consistency, leading to a deeper evidence base that informs drug discovery.
- Robotic process automation delivers a competitive advantage by analyzing patient data to predict the outcome of a treatment.
- Intelligent automation improves accuracy and efficiency in interpreting imaging scans and genomic data to identify patterns.
The benefits that accrue from automation include:
Cost savings: Improving efficiency means less expensive human capital expenditures are necessary, freeing budgetary resources for innovation.
Enhanced data accuracy: As the entire end-to-end clinical trial process is streamlined, new treatments are more likely to be approved.
Increased safety: Real-time monitoring and risk assessment tools help to identify potential safety issues early on, ensuring timely interventions.
Faster time to market: Accelerating the drug development process means new therapies can be brought to market faster for the benefit of patients.
USE CASES
Automation use cases for pre-clinical and clinical trial efficiency
AI algorithms, AI-led automation, RPA, chatbots, and ML models may be leveraged for pre-clinical and clinical trials, as detailed in the infographic below. AI helps in each stage of pre-clinical and clinical trial studies; let us take a closer look now.
Automation can simplify and speed up complex and time-consuming trials
Study design
The study design defines research questions, objectives, methods, and analysis for a clinical trial. Automation and machine learning are used for protocol design and language translation. Relevant data and information are culled from diagnostic health libraries and existing protocol insights to design a new protocol for a future study.
ML algorithms aid the design process by ensuring the study is designed optimally according to all quality control and regulatory needs. These systems also allow language translation services to be completed with much higher accuracy and reliability.
Study setup
The study setup manages the operational aspects of a critical trial, including budget resources and timeline. Machine learning is used to automate the development of case report forms (CRF) and practice databases built from the knowledge accumulated during the pre-clinical and clinical phases. The automation allows the system to analyze CRFs quickly and recommend real-time edits and improvements.
Validation reports ensure improvements are applied before the system goes live. ML is also used to automate the mapping of the Standard Data Tabulation Model (SDTM – a standardized format to organize and present data from clinical trials) and create SDTM-annotated studies.
Trial management
AI-assisted automation can be of use to ensure adherence to protocol and regulations in the day-to-day activities of a clinical trial.
Site selection: Sites with the required resources and expertise need to be identified for clinical trials. Machine learning algorithms help to inform site selection criteria by bringing different enrollment, safety, compliance, and data quality insights together. It helps to provide accurate predictions about which locations are best for a new study based on a particular medical practice area.
Patient enrollment: Eligibility criteria have to be met by patients who enroll for the trials, and they should also give informed or ethical consent. Predictive data analysis drives patient enrollment processes to ensure that considerations such as therapeutic practice areas, study duration, study complexity, the prevalence of adverse events, randomization, and disease prevalence, which are relevant to the validity and reliability of the research, are satisfied.
Risk-based monitoring: Risk-based monitoring (RBM) is essential to ensure patient safety and data integrity. An example of RBM involves categorizing high and low-risk patients when testing for a medication -obviously, a patient with comorbidities will need more monitoring.
Data management
Automating the collection, cleaning, organizing, and analysis of data collected throughout the clinical trial makes the process more efficient.
Smart queries: Potential errors and inconsistencies can be identified in data through this automated process. Smart query systems allow machine learning algorithms to analyze clinical trial data entries and explore which items can be highlighted for various field items. The system learns the expected value ranges and raises queries for issues outside the standard deviation.
Medical coding: Standardizing medical diagnoses and procedures facilitates accurate data analysis and reporting. Medical drug dictionaries such as WHODD and MedDRA are much easier to work with using rules-based parameters that ensure each piece of data is coded according to industry standards every time. The system will match the verbatim text of the study with the dictionary terms for the given practice specialty to ensure all codes are accurate and reliable.
Query management: Responding to queries is often a time-consuming process that can dramatically extend the time and cost it takes to complete clinical trials. Intelligent automation delivers the quality control necessary to reduce redundancy and deliver more accurate, complete, and reliable results by clustering queries according to importance.
Smart SDV: Source data verification or SDV helps to ensure that all data collection activities are optimized and data sets can be proofed as quickly as possible using electronic data capture technology.
Data analysis
Data analysis helps to interpret and generate insights from the collected data to answer all the research questions.
Automation and machine learning deliver robust utility in all data analysis, management, and utilization areas. Classification, clustering, and predictive analytics are just a few areas where these systems help to cull actionable, medically useful insights from disparate datasets.
Regulatory submission
The final endpoint of clinical trial proceedings is being able to hand off findings to regulators successfully. The process requires a substantial degree of documentation. Automation grants the control necessary to ensure that all versions of data are valid and reliable.
CSR automation: Clinical study reports are summaries of the findings of the clinical trial and are generated in a structured format for regulatory review using the Study Protocol and the Study Analysis Report (SAR). Under the current guidelines from the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) Guideline for Good Clinical Practice (GCP), natural language processing (NLP) may be used to streamline these processes.
CONCLUSION
Clinical trial automation challenges are many
While the potential benefits of implementing automation for pre-clinical and clinical trials are vast, there are some challenges, too. For instance, the initial investment costs may be high, the risk to patient privacy and data security is ever present, and integrating automation technologies with legacy software and training staff to use them is at times daunting. However, pharmaceutical companies that weigh the pros and cons and leverage healthcare software development to drive automation and accelerate the development of life-saving treatments for patients will stand to gain in the long run.
