In the last 10 years, Artificial Intelligence (AI) has emerged as a technological marvel which is expected to have a transformational impact on clinical research. Technological advances in recent years and especially the interest in AI, ML, etc., have created a new wave in the drug development areas, and are helping the pharma companies in using the information to develop drugs faster and with a greater chance of success in the early stages of drug development. The use of AI in healthcare has increased rapidly over the past few years, but much of the attention has been focused on the application of AI in healthcare, and applications of AI or ML, etc., in clinical research, are less frequently discussed.

Background

As clinical research is a broad subject and although essential to enhancing healthcare and outcomes, clinical research that is now conducted is intricate, labor-intensive, and expensive. These factors might occasionally jeopardize the application, acceptance, and implementation of clinical research.Due to the confluence of numerous new digital data sources and the computational ability to find significant patterns in the data using effective artificial intelligence, the future of clinical development is about to undergo a significant transformation. Clinical trial efficiency, generalizability, and success could all be enhanced by artificial intelligence (AI). Numerous professionals in the field are still unsure of the nature and operation of these technologies. Many people are uncertain of how to gobeyond the obstacles in the way of utilizing these technologies.

AI Comprehension in Clinical Research

AI is defined as “an entity (or collective set of cooperative entities), capable of receiving inputs from the environment, understanding and learning from such inputs, and exhibiting related and flexible behaviors and actions that support the entity’s achievement of a specific desired outcome over time.” Artificial intelligence (AI) comprises several approaches, including machine learning (ML), deep learning (DL), natural language processing (NLP), and optical character recognition (OCR). To generate predictions or choices, AI is frequently utilized in the pharmaceutical sector to develop data analysis algorithms and mathematical models that extract characteristics from sample data. CROs are employing a range of AI technologies to bolster their place in the global market amid the “artificial intelligence (AI) revolution” in the pharmaceutical and healthcare industries.
To improve trial success rates and reduce the cost of pharmaceutical R&D, AI has the ability to revolutionize critical stages of clinical trial design, from study conception through execution.

Protocol Development and Study Design Optimization

The success of the clinical trial is significantly influenced by its design. The cost, effectiveness, and likelihood of success of clinical trials are all severely impacted by poor research design. In order to develop trial procedures that address scientific concerns, researchers must consider factors such as population size, duration of the study, and eligibility requirements. Historically, trial design has been entirely dependent on clinical trial designers’ prior expertise. AI algorithms may aid to automate trial design and play a role in maximizing trial success and efficiency during the planning phase by using simulation approaches to vast volumes of data from earlier trials to assist trial protocol designing.AI algorithms may extract structured data from trial papers, forecast trial results using multimodal trial-related real-world data (RWD), and assess how each trial protocol element affects trial results. The development of AI has made it possible to use multimodal RWD for precise trial result forecasts at the preliminary stages. Using data from clinical trials, trial outcome predictors offer a general success likelihood.

Real-world data, such as clinical trial procedures or academic articles describing clinical trial findings, is essential for streamlining study design. Trial records, however, are semi-structured documents that contain both structured and unstructured text as well as result tables. When semi-structured data is not yet converted into structured forms like SQL queries, most AI models are unable to handle it. AI technologies called Natural Language Processing (NLP) techniques are used to extract crucial information from unstructured materials. NLP may be used to better develop study procedures by analyzing databases of clinical trials and real-world data. Since improved research design resulted in more predictable outcomes, shorter protocol development cycles, fewer protocol modifications, and increased study efficiency overall.

AI’s role in clinical trial participant management: Increasing the speed of patient enrollment and recruiting
Clinical trial participant management entails selecting target patient populations, recruiting patients, and retaining participants. Ironically, even though substantial resources are often spent on participant management, comprising time, preparation, and trial coordinator efforts, patient drop-out and non-adherence frequently lead trials to go over budget, take longer than expected, or fail to yield data that can be used. Patient cohort selection and recruitment processes that fall short in bringing the most suitable patients to a study on time, as well as a lack of technological infrastructure to handle the complexity of operating a trial, are two of the major reasons why a clinical trial fails. Patient recruitment is one of the most difficult aspects of clinical trials, but by utilizing artificial intelligence (AI), researchers might greatly increase efficacy and safety while lowering expenses. Patients are usually made aware of study options by healthcare professionals, such as clinical trial coordinators. However, the time necessary for clinical personnel to analyze medical records, treatment histories, imaging investigations, and laboratory data to find appropriate clinical trials for patients can be a substantial barrier to clinical trial enrollment. Problems with subject matching and enrollment lead to extensions of recruitment deadlines, which postpone the submission of trial protocols for regulatory clearances and, as a result, the product’s introduction to the market beyond the dates originally anticipated. Furthermore, because underrepresented communities might not respond positively to the intervention, selection bias might produce results that aren’t generalizable.

The Food and Drug Administration (FDA) recognized the following ways in which AI models and methodologies might improve patient cohort selection: (i) by lowering population heterogeneity; (ii) by selecting subjects who are more expected to have a clinical endpoint that can be measured, also known as “prognostic enrichment,” and (iii) by finding a population that is more likely to react to therapy, also known as “predictive enrichment.”

Patient Monitoring, Adherence, and Retention

Recruiting suitable patients for a clinical trial requires a significant time and financial commitment. Only by successfully completing the trial the return on investment can be achieved. Therefore, it is crucial that participants in the study continue to do so, abide by the trial’s policies and guidelines, and ensure that all data needed to track the effects of the tested medicine are accurately and effectively recorded. Patients must maintain thorough records of their medicine consumption as well as several other data points concerning their bodily functions, drug reaction, and daily routines to meet adherence standards. Dropouts resulting from noncompliance with trial protocols need further recruiting efforts, which lengthen trial times and incur significant extra expenditures. The burden of adherence may be reduced, endpoint detection can be done more quickly, and dropout and non-adherence rates can be decreased by using enhanced patient care and coaching techniques throughout ongoing studies. With the use of ML, two general strategies may be used to increase subject retention and protocol adherence. The first is to utilize AI to gather and analyze massive quantities of data to detect and intervene in participants who are at high risk of not complying with the research. The second strategy involves applying AI to lessen the workload associated with participant studies and enhance participant experiences. AI techniques combined with wearable technology provide new avenues for constructing such power-efficient, mobile, real-time, and tailored patient monitoring systems. By regularly retraining the underlying analytical DL models with new measurement data, this method enables the creation of disease diaries that are patient-specific and adaptable to any changes in illness manifestation and patient behavior. These illness diaries might be used as proof of adherence or a lack thereof. They would also collect data points for endpoint detection more effectively and reliably than existing patient-driven self-monitoring techniques since little or no human patient input is needed.

Data collection and management

The methods needed for data collection, management, and analysis may alter as a result of the application of AI in clinical trials. However, some of the challenges related to gathering real-world data and dealing with missing data can be addressed with the use of AI techniques. In some cases, patient-generated health data through wearable and other mobile/electronic devices can augment or even replace research visits and related traditional data gathering. Wearables and other technologies may make it possible to validate and apply novel, patient-centered biomarkers. An appealing application of AI, specifically natural language processing (NLP), to study data management is to automate data collection into case report forms, reducing the time, expense, and potential for error associated with human data extraction, whether in prospective trials or retrospective reviews.AI may also be used to process data. Semiautomated endpoint identification and adjudication have the potential to reduce time, cost, and complexity when compared to the current approach of manual event adjudication by a committee of clinicians, because, whereas endpoint adjudication has traditionally been a labor-intensive process, sorting and classifying events falls well within the capabilities of ML. AI may also be utilized to solve the problem of missing data in a variety of ways, including several sources of data missingness, data-related assumptions and goals, and data collecting and intended analytic methodologies. A clinical trial, registry, and clinical practice data are rich sources for hypothesis creation, risk modelling, and counterfactual simulation, and ML is ideally suited for these endeavors.

Clinical Trials in the Future: Not Just AI

Artificial intelligence (AI) won’t take the place of human ingenuity. AI cannot conduct trials independently or make scientific discoveries; it is not a panacea. This technology cannot, and is not intended to, replace scientists. It only exists to assist drug development specialists by offering an understanding of both the queries and responses that the technology facilitates us to ask. On the one hand, AI and machine learning will need to play a significant role if we are sincere about transforming how we discover and develop new drugs. As more individuals utilize these technologies to improve their lives, AI and machine learning will become more and more significant in not only clinical trial research and development but also other facets of health and wellness.

Decentralized clinical trials (DCT) was in existence for some time. However, COVID-19, the global pandemic has fundamentally escalated the reception of decentralized clinical trials as medical resources became consumed by SARS-CoV-2-related care. Travel became restricted by physical distancing. Institution of either a full or partial lockdown worldwide, and an increase in clinical trials conducted remotely and at participants, homes were seen. It was already taking a lot of effort to find and enroll subjects in trials, both large-scale trials, and trials where the pool of potential participants is relatively small.

How, then, to guarantee that enrolled subjects could still receive their dose under the everchanging circumstances?

Clinical trial decentralization has played a crucial part in this pursuit. A decentralized clinical trial approach utilizes innovation and technology rather than traditional trial practices, leaving choices for subjects who could not travel to the research site. It involves bringing an increasing proportion of a trial’s activities to the trial participants rather than using the traditional paradigm of bringing trial subjects to a trial site while remaining as patient-centric as possible. The pandemic has elevated the need for virtualization in both trial participants and trial contexts. Sponsors have also recognized the need to enroll a more geographically diverse pool of participants, representing a range of ethnic groups as well as ages and genders. Further facilitating better patient engagement and empowerment, as well as data capture. Decentralization broadens trial access to reach a larger number and potentially a more diverse pool of patients. DCT technologies such as electronic consent, mobile technology, telehealth, remote subject/data monitoring, wearables, sensors, electronic patient-reported outcomes (ePRO), an electronic trial master file (e-TMF), electronic medical imaging, etc. allow investigators to stay connected to participants in research without in-person visits. Decentralized trials can be ‘hybrid’ trials and need not be 100% virtual. These trials can maximize patient-centricity and encourage the transformation of healthcare and clinical research. Moreover, they offer the flexibility needed to enable higher patient inclusion without geographic limitations, balancing patient engagement with data integrity which yields higher satisfaction outcomes. Though not all disease conditions or study designs are DCT-friendly, there are several trials that can use decentralization design by being either entirely or partially virtual in a hybrid model. There are many operational, ethical, and regulatory benefits of conducting a decentralized clinical trial. That includes timely data collection, increased capacity for additional sites, greater control and comfort for participants, improved data quality, and clinical trial continuity.

Some key benefits of Decentralized methods:

• Enables virtual enrollment and participation of a more diverse trial participant population.
• Improves patient recruitment and retention.
• Increases patient compliance through eSuite services such as remote screenings, televisits, and user-friendly data capture devices.
• Deploys direct-to-patient services such as transportation, shipping, and in-home or mobile-nursing visits.
• Patients can report safety-related information in real-time, along with the quality-of-life parameters.
  Improves reliability and data integrity.
• Capturing data directly from patients using technology such as mobile phones, wearable devices, bio-sensors, and electronic patient-reported outcomes (ePRO) eliminates manual data entry. It keeps data organized and safe by reducing errors.

The Patient Journey in a Decentralized Trial

The journey for the decentralized clinical trial path for patients involves enrollment, randomization, and distribution of investigational Medicinal Products(IMP). Data capture at multiple points directly from patients through wearable technology and sensors allows the patient to be studied in real-life situations. Decentralization capabilities enroll a more diverse trial participant population remotely and facilitate the real-time connection of patients with investigators. Further, allowing more secure experiences throughout their clinical trials.

In a decentralized trial, the use of electronic signatures (eConsent) in place of a physical or wet signature has been implemented. As eConsent is easier to use and understand, can be applied on-site or remotely, and is faster and secure. Additionally, AI (Artificial Intelligence) and ML (Machine Learning) have the potential to transform clinical trials, creating a more reliable and secure method of trial randomization. Even in patients who give consent, an alarming dropout rate across all clinical trials has been observed. However, the dropout rates could be significantly declined by reducing the burden of travel from the patients. Initiation of IMP dispensation from a depot and enabling Direct-to-Patient (DTP) shipmentssolve the challenges associated with continued site visits and site supply responsibility.

In decentralized trials, data reliability and integrity have been achieved through agile technologies, including wearable watches and biosensors, In-home devices, and data-capturing tools such as eCOA and eCRF. These tools provide studies with faster and more accurate data with little to zero transcription errors. With Remote Source Review, the clinical research associates are alerted as soon as the errors are detected, allowing them to virtually access and review the source document.

Ultimately, Decentralized Clinical Trials address an opportunity to reevaluate the conventional clinical trial model while keeping the patient at the center of this arising worldview. Sponsors have seen the advantages DCTs and patients genuinely value the booming flexibility and comfort.

It had been a year since the Novel Corona Virus (COVID-19) outbreak happened. Scientists had continuously strived to invent a vaccine, and finally, many pharmaceutical companies are coming up with their interventions. With the few of the clinical trials coming to a climax, the Pharma companies and Governments are pondering on the distribution of vaccines to billions of people and their supply chain management which poses as one of the major challenges.

From the time of manufacture till distribution, vaccines must be continuously stored in a limited temperature range, according to the type of vaccine. Different vaccines have different storage conditions like ambient, freeze-sensitive or frozen. Since vaccines are parenteral, optimal temperature and storage are to be taken care of to prevent any leakage and decrease in the efficacy of the vaccine. In order to tackle this challenge, WHO has recommended certain ‘cold chain’ principles to manage the vaccine roll out?

A temperature-controlled supply chain to maintain the quality of the product, in the desired low-temperature, is referred to as a Cold chain. A cold chain is said to be uninterrupted when there is controlled production, storage and distribution in the desired temperature using appropriate equipment and logistics. The cold chain is sometimes referred to as a vaccine supply chain or the immunization supply chain. There are a set of guidelines designed to keep the vaccine in WHO recommended temperature from the point of supply to the point of administration.

To maintain a reliable vaccine cold chain at the peripheral level, we have to

• Store vaccines and diluents within the required temperature range at all sites
• Pack and transport vaccines to and from outreach sites according to recommended procedures
• Keep vaccines and diluents within recommended cold chain conditions during immunization sessions.

Cold chain logistics also referred to as chill chain logistics refers to the transport of temperature-controlled products like bio-pharmaceuticals, foods etc.

Requirements for Vaccine:

The cold chain has three key components: equipment for transport and storage, technical expertise, and effective procedures for management. In order to ensure secure vaccine transport and storage, all three components must be combined.

Proper cold chain management depends on proper temperature monitoring. Minor temperature excursion events (when the vaccine gets stored in temperature range outside the manufacturers’ recommendations), can impact the vaccines’ potency. Thermometers and data loggers should be used to monitor the transit temperature, and thermometers should be used for monitoring temperatures in the storage locations at sites of vaccinations, which must be continuously monitored.

Biologicals are sensitive to Heat, Light and Freezing.

The heat and freezing sensitive vaccines are categorized from Group A, which is more sensitive to heat and freezing to Group F, which are less sensitive to heat and freezing. Analyzing many observational studies, WHO has recommended +2 to +8°C as an optimal temperature for the vaccines which can be stored in normal refrigerators.

Freeze-sensitive vaccines produce adjuvants of aluminum which, when subjected to freezing temperatures, irreversibly lose potency. Freeze-sensitive vaccines require storage at 2 to 8°C and can lose potency even without visible signs that freezing has occurred when exposed to sub-zero temperatures.

Vaccines lose efficacy upon light exposure. To prevent the loss of vaccine efficacy, dark vials are used.

Equipment which are required for Cold Chain:

Different types of equipment are listed under the national cold chain system for transporting of the vaccines and storage of the vaccine. In the national cold chain system, we have three levels of hierarchy, i.e. Primary, Intermediate and Peripheral level. WHO has specified specific prequalification standards to ensure efficient performance of cold chain equipment?

Standard equipment for Cold Chain.

Refrigerators:

The most reliable power supply should be chosen for the refrigerator, i.e. either electrical, solar or gasoline. The storage capacity should fit for vaccine and water pack storage.

Cold boxes:

During transportation, cold box is used as an insulated container which can be lined with water packs for storage of vaccines and diluents in required temperature.

Vaccine carriers:

Vaccine carriers are easier to carry as they are smaller in size compared to other equipment. Current prequalified vaccine carriers have a cold life with frozen ice packs between 18 to 50 hours at +43 °C and a cool life with cool water packs.

For both cold boxes and vaccine carriers, the ice packs need to be conditioned before packing. Conditioning of ice packs involves completely frozen icepacks to be left at room temperature for a short time (around 30 minutes). This conditioning prevents the vaccines from getting frozen inside the cold box or vaccine carriers.

Water packs:

Water packs are used to line the interior of the cold box/ vaccine carrier. These are flat, leak-proof plastic containers that can be filled with tap water.

Foam pads:

It is a soft sponge which fits on top of the water packs inside a vaccine carrier.

Conclusion:

India being highly denser population where cold chain involves lots of inventory management, traceable trackers and appropriate storage since biologics are sensitive to minor changes of heat, light and cold. Planning and organization can help us in making the cold chain supply in rolling out the vaccine effectively.

India, being the second most affected country with COVID-19, Government of India is clearing the ground for vaccine rollout and mass immunization, as CDSCO is approving the vaccine use in India. It is pushing its pre-existing cold chain suppliers to augment the requirements based on various players in the market. While Pfizer vaccine recommends a storage temperature of -70°C±10°C during transit and 2-8°C for storage up to five days, Moderna wants the vaccine to be frozen at -20°C during transit. With such restrictions, India’s cold chain logistics industry is gearing up to meet the requirement, but the task looks difficult (though not unachievable being optimistic). Having the vaccine stored and transported at this temperature requires major planning and involves expenditure. The challenge is to get the vaccine transported and distributed to the vulnerable population in remote parts of the country and also to maintain the capability to transport such bulk load without affecting the vaccine. The Indian company vaccines are better when compared for the storage conditions where the manufacturers like Serum and Bharath Biotech have said that their vaccines can be stored in regular refrigerators at 2-8°C. Therefore, in this case the investment will be smaller comparatively.

With the COVID-19 pandemic slump hitting out at all people, together with lockdowns announced in the country, there were significant impacts on all the businesses big and small. But with this phase, the cold chain industry is going to have significant growth and is sure to get revamped for good, and we hope so.

The pandemic lockdown and quarantine has slowed down or in some cases paused most of our everyday lives. With daily routines affected, there is a butterfly the effect that ended up affecting most professions in some or the other way- including the clinical research industry.

With the whole world focusing on COVID19 research, much of non-COVID19 related ongoing and upcoming projects have taken a backseat – especially, in-person operations. This doesn’t necessarily mean a complete halt in the research process, regulatory bodies like USFDA have issued guidelines on conduct of clinical trials during COVID-19 pandemic. Although these guidelines are designed to tackle current pandemic, these changes are bound to have short term implications that could potentially shift the way in which the industry functions in the long term.

Effects on research operations/procedures/ trial progress:

Trial sites that have been transformed or dedicated for COVID-19 treatment may not be capable of recruiting or conducting future trials. At the same time, the recruitment and conduct of ongoing trials have taken a significant hit with most of the investigators and healthcare personnel catering to COVID patients and pandemic. Especially in our country where the patient to doctor ratio is high enough in general circumstances, doctors involved in trials might have to shift their focus on providing COVID related services and their availability at trial site might be irregular.

Transport restrictions during lockdown have disrupted supply chain of investigational products since the investigational products cannot reach the trial site, products under manufacture are on hold due to non-functional or dysfunctional manufacturing units and unavailable labor. Moreover, global trials might face delays in product delivery since most countries have closed their borders for international travel, import and export.

In the early stages of the pandemic, the question was of taking critical decisions on withholding or postponing trials in the recruitment or pre-recruitment stage for trial sponsors. A significant impact of lockdown can be observed on global trials with respect to difference in trial conduct due to varied pandemic related restrictions like some countries are able to conduct trials as per schedule, while others are not able to.

So far, the regulatory bodies including CDSCO have issued guidelines with respect to the challenges in trial conduct for continuity and progress of ongoing trials. These guidelines mandate sponsors and CROs to take necessary actions such as protocol deviations while prioritizing patient safety and data integrity.

However, several ethics committees or review boards might be temporarily non-operational due to lockdown restrictions which could pose as a considerable hurdle for obtaining regulatory clearances and approving the mandated amendments for ongoing or upcoming trials. This is a major challenge since a protocol deviation would require the sponsor/CRO, regulatory authorities and ethics committees to coordinate. As companies learn to adapt and consider implementing new strategies to keep clinical trials on track, in many cases the only option is to temporarily pause or delay development programs.

Effects on trial participants:

Trials with the following population would face most of the challenges because of the pandemic: –

• enrolled populations, including the elderly, the immunocompromised, and patients with pulmonary conditions (e.g., COPD)

• Have primary or secondary endpoints that require in-person visits or hospital infrastructure and equipment (e.g., CT and PET scans) for assessment.
• Trials that require in-person screening.
• Involve indications with minor safety or quality of life implications for patients (e.g., “lifestyle drugs”) and indications where a patient’s environment has a significant impact on therapeutic success (e.g., psychiatric and neurological indications)
• Are currently in the patient recruitment stage or are in Phase 1 with healthy participants
• An infection in the participants may affect the outcomes of the study and in some cases, some participants may need to be withdrawn from the trial.

These consequences could be especially devastating for trials in rare diseases, where available patient population is very small.

Effect on companies /research organizations:

Delays in projected launch timelines could occur as a result of delayed clinicaltrials. This may in turn lead to shorter time period of patent validity and lower near-term revenue forecasts. Changes in launch timelines have the potential to alter competitive scenarios in many therapeutic areas and other facets of the industry, where in the opportunities are up for grabs for whoever adapts and responds to pandemic related changes exceptionally.

Companies, especially small scale, have to adapt to renewed guidelines and switch to operating remotely, or re-evaluate efficiency of current mode of operations, while following the prescribed regulatory measures. This in turn could be a significant problem for those who weren’t adept with appropriate resources in order to make necessary transitions in their mode of operations. The impending economic crisis would only weigh into this un-resourcefulness. This is particularly challenging for countries (like in Europe) where legalities such as General Data Protection Regulation (GDPR) could obstruct the remote functioning of trials.

Pricing negotiations also could be impacted by the pandemic, especially for some non-COVID-19 therapies (e.g., the third or later drug to market in a class) which are perceived to be of low unmet need. New recruitments into companies be on hold due to postponed graduations and current workforce might take a hit because of economical setbacks that could ultimately effect company productivity.

Conclusion 

Like any other industry Clinical research industry should come up with some solutions to fix the problems associated with COVID-19 pandemic. The obvious solutions recommended are decentralizing and partnering. For example, home visits, shipping investigational products to participant’s residence, sample collection from home or nearby pharmacies can help performing a clinical trial even in lockdown. Instead of halting, it is time to resume the clinical operations but close monitoring of situations and safety of research staff should remain a priority.

Clinical trials  also called as clinical research designed to evaluate safety and efficacy of promising therapeutic products having potential to reach market. Abiogenesis Clinpharm helps in conducting trials with ideal design considered optimum for a therapeutic product. As a best clinical research organisation in India, the team is well versed with appropriate recruitment strategies, quality procedures and essential data handling. We provide services in regulatory, medical writing and medical monitoring for clinical trials along with statistical analysis, clinical data management and post-marketing surveillance studies to the clients.Concerns on meeting sponsor timelines are exclusively addressed to get timely recruitment and putting operational methods in place which makes conduct of the trial easier.  Abiogenesis Clinpharm Private Limited would be the best partner for pharmaceutical, Nutraceutical and Medical Device Companies worldwide to carry the development of their promising and potential therapeutic products.

Phases of Clinical Trials

Phase I studies: New investigational product tested in smaller groups of healthy volunteers (SAD: Single Ascending Dose or MAD: Multiple Ascending Dose).

Phase II studies: Product is investigated in smaller number of patients to explore ideal dose with better efficacy & safety.

Phase III studies: Investigational product is tested in large number of patients from multiple centers to confirm the suitable dose for marketing approval.

Phase IV studies: Following a product approval, post-marketing trials are conducted to monitor safety and efficacy for long-term risks and benefits including detection of rare or very rare side effects.