Decentralized clinical trials and in silico clinical trials

These days, the buzzword in the clinical trial field is 'decentralized clinical trials' or DCTs. The Covid-19 pandemic seems to push the clinical trials toward 'decentralized' or 'hybrid' of decentralized and traditional clinical trials. 

Traditional clinical trials are 'centered' around the clinical trial sites and the investigators. The patients (clinical trial participants) are recruited by the investigators who are the medical doctors responsible for the conduct of the clinical trial at trial sites. The trial sites are the clinics, hospitals, and medical centers. The patients would need to visit the trial sites regularly to see the investigators for clinical trial activities (signing the informed consent, screening for eligibility, receiving study treatments, performing efficacy and safety measures,...). The clinical trial data will then be recorded and entered into the database (for example EDC) by the study coordinator or investigator at investigational sites.

Decentralized clinical trials are defined as the decentralization of clinical trial operations where technology is used to communicate with study participants and collect data and the data collection will not depend on the frequent patient's visits to the investigational sites. According to CTTI (clinical trial transformation initiative) Recommendations: Decentralized Clinical Trials: 

DCTs using telemedicine and other emerging and novel information technology (IT)
services offer the potential for local HCPs to participate in clinical trials. This may
provide several advantages compared to traditional clinical trials conducted at more
centralized clinical trial sites, including the following:

• Faster trial participant recruitment, which can accelerate trial participant access to important medical interventions and reduce costs for sponsors.
• Improved trial participant retention, which may reduce missing data, shorten clinical trial timelines, and improve data interpretability.
• Greater control, convenience, and comfort for trial participants by offering at home or local patient care.
• Increased diversity of the population enrolled in clinical trials.
• An opportunity for home administration or home use of the IMP, which may be
• more representative of real-world administration/use post-approval.

FDA's Advancing Oncology Decentralized Trials - Learning from COVID-19 Trial Datasets also listed the advantages of the DCTs: 

• Decentralized Clinical Trials (DCT) may have several potential benefits including reduced patient and sponsor burden and increased accrual and retention of a more diverse trial population.
• Use of full or hybrid DCT designs by commercial sponsors has been rare in oncology, in part due to uncertainty surrounding the effect of remote assessments on data quality and outcomes.
• COVID-19 has necessitated DCT-type trial modifications such as remote assessments to reduce patient exposure to COVID-19 infection from travel to trial sites.
• Many of these remote assessment modifications were deployed in the middle of large ongoing cancer trials.
• There is an opportunity to evaluate the effect of remote assessments on trial data to advance Decentralized Trials in oncology.
• Better understanding of the effect of DCT modifications can reduce uncertainty for sponsors and regulatory bodies, and identify mitigation strategies for future prospective DCT designs.

Historically, DCTs may be called differently: virtual trials, siteless trials, remote trials, digital trials, direct-to-patient trials. These different names may just describe one specific aspect of the DCTs and can cause confusion. For example, 'virtual trials' can be confused with the in silico trials which are based on computer models and do not use real participants (patients) but computer programs to model participants to assess drug efficacy and safety during the preclinical phase or before a traditional trial.

"Decentralized clinical trial" is a “Terrible Name for a Promising Innovation” and is not a best terminology for patients and study participants. Alternative names such as direct-to-patient trials, patient-centric trials, and home-based trials seem to be more straightforward and better terms.

In essence, in traditional clinical trials, we bring the trial to the patients; in DCTs, we bring the patients to the trial.

There are still a lot of challenges and obstacles to implementing decentralized clinical trials. Application of DCTs may be limited to some special situations (such as post-marketing studies with patient-reported outcomes and outcomes measured digitally). It is still rare for pivotal and registration studies to use full DCTs. It seems to be more appropriate to adopt hybrid trials - the combination of the traditional and the decentralized trials. For example, in clinical trials in the rare disease area, it is difficult for patients to travel to the investigational sites, the patients may visit the investigational sites for some important visits (in-clinic visits) and then the home health care nurses may be used for in-home visits to patient's home.  

In 2019, Janssen, PRA Launch a Fully Virtual Trial (it should be called the decentralized trial) "A Study on Impact of Canagliflozin on Health Status, Quality of Life, and Functional Status in Heart Failure (CHIEF-HF)". The study design was described in the Circulation: Heart Failure "Novel Trial Design: CHIEF-HF". CHIEF-HF seems to be the first phase III trial being fully decentralized.

 

Here are some references for DCTs:   

• Defining Decentralized Clinical Trials and Understanding Their Nuances
• Decentralized clinical trials: Collaboration is needed to drive their adoption and impact
• DTRA.org Decentralized Trials Research Alliance
• FDA Official Reviews “Challenges and Obstacles” to Decentralized Trials
• Decentralized Clinical Trials - Craig Lipset - Founder of Clinical Innovation Partners
• Is The Advent Of Decentralized Clinical Trials A Blessing Or A Curse?
• ACRO: decentralized clinical trials
• 
  

The decentralized clinical trials still collect the data from the patients and should not be called 'virtual clinical trials'. On the contrary, the 'In Silico clinical trials' is more appropriately called 'virtual clinical trials' or 'patientless clinical trials' and it simulates the virtual subjects for modeling and prediction. In Silico clinical trials may use the data from pre-clinical and historical clinical trials for simulation but involves no real patients in the study.  

According to the senator bill "AGRICULTURE, RURAL DEVELOPMENT, FOOD AND DRUG

ADMINISTRATION, AND RELATED AGENCIES APPROPRIATIONS BILL, 2016", In Silico clinical trials use computer models and simulations to develop and assess devices and drugs, including their potential risk to the public, before being tested in live clinical trials."

 

In Silico clinical trials are part of the model-informed drug development (MIDD). the FDA has a MIDD pilot program managed by the Division of Pharmacometrics. 

Dr. Yaning Wang has multiple presentations promoting the MIDD and In Silico clinical trials, for example, in his presentation at the 2021 FDA Science Forum "Regulatory Applications and Research of Model-Informed Drug Development (MIDD)" (Youtube video at 2:38:25) and in his presentation at PMDA "Application of MIDD in New Drug Development and Approval". 

Here are some additional references on In Silico clinical trials:

• Pink Sheet (2021) Why 2020 Saw The Steady Rise Of In Silico Trials
• Chao Liu at FDA Model-informed analysis during NDA/BLA Review - Insights from two FDA case reviews
• FDA-ISoP Public Workshop: Model Informed Drug Development (MIDD) for Oncology Products
• Scott Berry "In Silico Clinical Trial Design"
• Insilicotrials.gov blog 

Synthetic Control Arm (SCA), External Control, Historical Control

Lately, the term 'synthetic control' or 'synthetic control arm' or SCA, in short, is becoming popular - it is mainly driven by the desire to design more efficient clinical trials that are not traditional, the golden standard RCT (randomized controlled trials) with a concurrent control group. 

In a previous post, I compared historical control versus external control in clinical trials. The subtle difference is mainly in the time element. Historical control is one type of external control, but the reverse is not true. External control can be historical control or contemporaneous control. For example, in a clinical trial to assess the efficacy and safety of the donor lung preserved using ex-vivo lung perfusion (EVLP) technique, the EVLP lung transplantation cohort was compared to a contemporaneous (not concurrent) control cohort that was formed through the matched control from the traditional lung transplantation patients.   

Then what is 'synthetic control' or 'synthetic control arm'?

Synthetic control arm is the use of synthetic data as a control arm in clinical trials. According to an article "Synthetic data in the civil service" in the latest issue of SIGNIFICANCE, synthetic data is defined as "artificially generated data that are modelled on real data, with the same structure and properties as the original data, except that they do not contain any real or specific information about individuals. The goal of synthetic data generation is to create a realistic copy of the real data set, carefully maintaining the nuances of the original data, but without compromising important pieces of personal information."

Synthetic control arm is a control arm generated through existing data resources representing normal patient statistics. Synthetic control arm can serve as a comparator for a single-arm clinical trial or augment the smaller concurrent control group (for example with active:control ratio of 3:1 or 4:1) in RCTs. 

In a presentation by at Harvard Medical School Executive Education Webinar Series,  Mr. Chatterjee presented "Synthetic Control Arms in Clinical Trials and Regulatory Applications" and he defined the 'synthetic control arm' as the following:

In a paper by Thorlund et al "Synthetic and External Controls in Clinical Trials – A Primer for Researchers", they stated that synthetic control arms are external control arms - two terms can be used interchangeably:

External control arms are also called “synthetic” control arms as they are not part of the original concurrent patient sample that would have been randomized into the experimental or the control treatment arms as in a traditional RCT. External controls can take many forms. For example, external control arms can be established using aggregated or pooled data from placebo/control arms in completed RCTs or using RWD (Real World Data) and pharmacoepidemiological methods. Pooled data from historical RCTs can serve as external controls depending on the availability of selected “must have” data, similarity of patients, recency and relevancy of experimental treatments that were tested, availability and similarity of relevant endpoints (eg, operational definitions and assessments), and similarity of other important study procedures that were conducted in these historical trials. It is important to note that using control data from historical RCTs still results in a nonrandomized comparison but has the advantage of standardized data collection in a trial setting and patients who enroll in clinical trials may have more similar characteristics than those who do not.

However, I think that there are subtle differences between these two terms. With 'synthetic' control arms, the term 'synthetic' implies there are some selection, manipulation, derivation, matching, pooling, borrowing from the source data. Just like the meta-analysis is also called research synthesis and requires the statistical approaches to combine the results from multiple scientific studies, the 'synthetic' control also requires the use of statistical approaches to process the data from multiple sources to form a control group to replace the concurrent control in traditional RCT clinical trials. 

The source data for constructing synthetic control can be the data from previous RCT clinical trials, real-world data, registry data, data from natural history studies, electronic health records, ... The source data must be the subject-level data, not the summary or aggregate data. 

ICH E10 "CHOICE OF CONTROL GROUP AND RELATED ISSUES IN CLINICAL TRIALS" included "External Control (including Historical Control)" as one of the options as the control groups in clinical trials. The external control here is not the same as synthetic control. 

1.3.5 External Control (Including Historical Control)

An externally controlled trial compares a group of subjects receiving the test treatment with a group of patients external to the study, rather than to an internal control group consisting of patients from the same population assigned to a different treatment. The external control can be a group of patients treated at an earlier time (historical control) or a group treated during the same time period but in another setting. The external control may be defined (a specific group of patients) or nondefined (a comparator group based on general medical knowledge of outcome). Use of this latter comparator is particularly treacherous (such trials are usually considered uncontrolled) because general impressions are so often inaccurate. So-called baseline controlled studies, in which subjects' status on therapy is compared with status before therapy (e.g., blood pressure, tumor size), have no internal control and are thus uncontrolled or externally controlled.  

How to Create a Synthetic Control Arm? 

The first step of creating a synthetic control arm is to harmonize the source data. The data from different sources or from different clinical trials should be standardized so that they can be used for the synthesis process. 

Various statistical approaches can be used to create a synthetic control arm. In an audiobook on synthetic control arms by Cytel, propensity scoring and Bayesian Dynamic Borrowing methods were discussed. 

The synthetic control arm can be considered as an approach of 'borrowing control' - i.e., some controls are borrowed from historical data. There are numerous options for borrowing controls: 

• Pooling: adds historical controls to randomized controls 
• Performance criterion: uses historical data to define performance criterion for current, treated-only trial to beat 
• Test then pool: test if controls sufficiently similar for pooling 
• Power priors: historical control discounted when added to randomized controls
• Hierarchical modeling: variation between current vs. historical data is modeled in Bayesian fashion 

In the article by Thorlund et al, the pros and cons of different methods for generating synthetic control arms were discussed. 

In Mr Chatterjee presentation, "Synthetic Control Arms in Clinical Trials and Regulatory Applications", there is a diagram to describe the process for creating a synthetic control arm. 

Even though the synthetic control arms, the use of real-world data, conducting the single-arm clinical trials are very appealing, the challenges are ahead and the regulatory acceptance is uncertain. There may be limited use in special cases (such as ultra-rare diseases, pediatric clinical trials) and for post-marketing activities (such as label expansion, label modification, post-marketing studies), but not in prime time to replace the concurrent control in traditional RCTs. 

In an article at Statnews.com "Synthetic control arms can save time and money in clinical trials", 

Even with the FDA making the use of real-world data a strategic priority, synthetic control arms can’t be used across the board to replace control arms. Synthetic control arms require that the disease is predictable (think idiopathic pulmonary fibrosis) and that its standard of care is well-defined and stable. That certainly isn’t the case for every disease.

It’s also important to consider that even when information is available from real-world data sources, it may be difficult to extract or of low quality. Routinely captured health care data, such as electronic health records, are typically siloed, fragmented, and unstructured. They are also often incomplete and difficult to access. New tools and methodologies are needed to consolidate, organize, and structure real-world data to generate research-grade evidence and ensure that confounding variables are accounted for in analyses. Analytic techniques such as natural language processing and machine learning will be needed to extract relevant information from structured and unstructured data.

The same view is also expressed in a Pink Sheet article "External Control Arms: Better Than Single-Arm Studies But No Replacement For Randomization".

Synthetic control group derived from historical clinical trial data could augment smaller randomized trials and yield better information than single-arm studies, but this approach should not be viewed as a substitute for randomized trials where feasible

ADDITIONAL REFERENCES:

• Alberto Abadie (2020) Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects
• Cytel Ebook (2020) Demystifying synthetic control arms
• Carrigan et al (2019) Using Electronic Health Records to Derive Control Arms for Early Phase Single-Arm Lung Cancer Trials: Proof-of-Concept in Randomized Controlled Trials
• Lim et al (2018) Minimizing Patient Burden Through the Use of Historical Subject-Level Data in Innovative Confirmatory Clinical Trials: Review of Methods and Opportunities
• Burcu et al (2019) Real‐world evidence to support regulatory decision‐making for medicines: Considerations for external control arms
• Ghadessi et al (2020) A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG)
• FDA-AACR Workshop: Real-world Evidence (July 29, 2019) Slide Decks
• Elizabeth Stuart The use of synthetic control and other covariate adjustment strategies for policy evaluation 
• Synth: An R Package for Synthetic Control Methods in Comparative Case Studies or Synth Package
• Exploring whether a synthetic control arm can be derived from historical clinical trials that match baseline characteristics and overall survival outcome of a randomized control arm: Case study in NSCLC
• Berry et al (2017) Creating a Synthetic Control Arm (SCA) from Previous Clinical Trials: Applications to Establishing Early Endpoints as Indicators of Overall Survival in Relapsed/Refractory Acute Myeloid Leukemia - poster  and Abstract

Are we become slaves of the Intent to Treat Principle?

The intent to treat (or intention to treat) principle was invented by the statistician about 30 years ago. It took a while for the clinical trial community to accept the this concept. Nowadays, the intent to treat principle has been well accepted by the people well beyond the statisticians. However, I don't think everybody really understand the concept even though he (or she) may mention the intent to treat pricinple every time he (or she) can. I have been really bothered by the comments from regulatory reviewers to suggest us to define an intent to treatment population for studies without randomization and without placebo or active control (for example, a dose escalation study). We seem to become slaves of the intent-to-treat.

In a lot of situations, the intent to treat principle is misunderstood. The intent-to-treat concept is tied with randomization for treatment allocation. No randomization, no Intent-to-treat.
The intent-to-treat concept is really for the large scale, confirmatory, pivitol studies. For very ealier stage studies (for example, the dose escalation studies) with very few subjects, there is no need to follow the intent-to-treat principle.

Intent to treat population includes all randomized patients in the groups to which they were randomly assigned, regardless of their adherence with the entry criteria, regardless of the treatment they actually received, and regardless of subsequent withdrawal from treatment or deviation from the protocol. Stricly according to the intent to treat principle, if a subject is randomized, but never receive study medication, the subject would be included in the statistical analysis; if a subject is randomized to drug A, but wrontly takes the drug B, the subject would be analyzed in treatment group A, not B (so called as randomized, not as treated); if a subject is randomized, but with no outcome measures, the subject would be included in the analysis with subject considered as treatment failure.

Intention to treat analyses are done to avoid the effects of crossover and drop-out, which may break the randomization to the treatment groups in a study. Intention to treat analysis provides information about the potential effects of treatment policy rather than on the potential effects of specific treatment.

To apply the intent to treat principle, an appropriate method for handling the missing data needs to be specified. A popular practical approach (not idea approach from statistical standpoint) is last value carried forward.

Intent to treat principle is not needed for all clinical trials and should not be interpreted as "include all enrolled subjects" or "all subjects who signed informed consent". The intent to treat is from the randomization standpoint, it has nothing to do with "study subject has intention to be treated in the clinical trial".

References:
1. ICH guidance E9 http://www.fda.gov/cder/guidance/ICH_E9-fnl.pdf
2. My presentation on ITT vs mITT http://webspace.webring.com/people/eu/um_3826/ITT_mITT_JSM2004.ppt
3. Wikipedian http://en.wikipedia.org/wiki/Intention_to_treat_analysis

Phase 0 clinical trial

Traditionally, we have been talking about the phase I to phase IV clinical trials in drug development. We start from the small trials in healthy volunteers (phase I) to dose finding or proof of concept (POC) trials (phase II) to pivotal trials (Phase III), the post-marketing trial (Phase IV). Or we start from trials for MTD (maximal tolerable dose) - Phase I to trials for MED (minimal effective dose) - Phase II. Now there comes a new phase of clinical trial - Phase 0.
According to wikipedia, "Phase 0 is a recent designation for exploratory, first-in-human trials conducted in accordance with the U.S. Food and Drug Administration’s (FDA) 2006 Guidance on Exploratory Investigational New Drug (IND) Studies.[7] Phase 0 trials are also known as human microdosing studies and are designed to speed up the development of promising drugs or imaging agents by establishing very early on whether the drug or agent behaves in human subjects as was anticipated from preclinical studies. Distinctive features of Phase 0 trials include the administration of single subtherapeutic doses of the study drug to a small number of subjects (10 to 15) to gather preliminary data on the agent's pharmacokinetics (how the body processes the drug) and pharmacodynamics (how the drug works in the body).
A Phase 0 study gives no data on safety or efficacy, being by definition a dose too low to cause any therapeutic effect. Drug development companies carry out Phase 0 studies to rank drug candidates in order to decide which has the best PK parameters in humans to take forward into further development. They enable base go/no go decisions to be based on relevant human models instead of relying on animal data, which can be unpredictive and vary between species."

While the term 'phase 0' is fancy and novice, the usefulness of phase 0 trials needs to be proved in the future. At this point, I guess it is just a concept from governmental agencies such NCI (national cancer institute). I doubt that the industry will really be interested in this Phase 0 trial.

Translational Medicine

I have heard the term "evidence-based medicine", "socialized medicine", "individulized medicine", now there is a new term "translational medicine".

" Translational medicine is the continuum – often known as "bench to bedside" – by which the biomedical community takes a focused point of view to move research discoveries from the laboratory into clinical practice to diagnose and treat patients.

Translational medicine is often used synonymously with "Molecular Medicine" and "Personalized Medicine", both of which are used to refer to the process of applying molecular insights from laboratory discovery to clinical care.
Specifically, today’s process of translational medicine involves:

• A scientific search to discover the origins and mechanisms of disease
• The identification of and insight into specific biological events, biomarkers, or pathways of disease
• The use of such insights to systematically discover and develop new diagnostics and therapeutic methods and products
• The adoption of such new diagnostic and therapeutic approaches into the routine standard of care

Translational medicine represents a paradigm shift in the biomedical research enterprise. Traditionally, research, drug development, and clinical medicine were three virtually separate endeavors: bench scientists, drug developers, and clinical researchers rarely, if ever, met together, shared ideas, or even used the same vocabulary.

This dramatic change has come about in recent years as a result of the genomics and bioinformatics revolution. Patients provide the biospecimens from which "disease signatures" at the molecular level can be identified and are then used to develop diagnostics and drugs targeted at sub-groups of disease. The role of patient advocates has also been critical to this change in research and clinical care. They have catalyzed a more patient-centric approach to medicine."

A good example is the recent publication in Nature Medicine talking about the potential effect of Avandia on Osteoporosis. See the weblink below:

http://www.nature.com/nm/journal/vaop/ncurrent/abs/nm1672.html