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Clinical Trial Simulation:An Efficient Tool for Improved Study Designs, Dose Selection, and Go/No-Go Investment Decisions

An Executive Summary

How can modeling and simulation improve the clinical trial go/no-go decision-making process, accelerating drug development?

OverviewIn July 2017, US Food and Drug Administration (FDA) Commissioner Scott Gottlieb, MD, announced the agency’s Innovation Initiative to support “in silico tools in clinical trials for improving drug development and making regulation more efficient.”1

In silico clinical trials involve the use of computer models and simulations to develop and evaluate devices and drugs. Modeling and simulation involve taking critical information about the human body, the disease, the drug, how they interact, and how such interactions change over time, and using mathematical equations and statistics to create a model, which is a description of these relationships. We can then use the model to create an imitation of reality by simulation. In clinical trial simulation, we apply a study protocol to the model, thus recruiting

“virtual” subjects, dosing them and making observations based on the model. The informa-tion we gather can help shape study design, accelerate the drug development process and increase regulatory efficiency.

Why Model and Simulate?Using models, we can integrate information over time, across dose levels, among subjects, across different studies and from other drugs, even combining data from in-vitro, animal and man. Simulations based on these models can help us optimize future studies and predict outcomes of scenarios we have not yet studied. We can quantify variability and uncertainty as well as identify the gaps in the knowledge used to build the model.

In addition to using such models to guide and justify dose selection and study designs, pharmaceutical companies have saved or reduced their R&D costs by as much as $500 million using modeling and simulation techniques that increased clinical study success rates and improved drug-development–related decision-making.2 At the FDA, the Agency’s use of models and simulations allows it to review and assess sponsors’ clinical study designs that support effectiveness, optimize dosing, and predict safety and adverse events.

On a nuts-and-bolts level, models can help answer questions like:• What is the minimum efficacious and safe dose?• How do dose regimens compare?• What are the differences between the study and comparator drugs?• How big a patient population is needed to achieve adequate power?

Per Olsson Gisleskog Principal Consultant

SGS Exprimo

Alberto Russu Senior Scientist Pharmacometrics,

Global Clinical Pharmacology Janssen Research and

Development

CLINICAL TRIAL SIMULATION

• What is the minimum duration of treatment?

• What is the effect of treatment with more than one drug?

• Are there differences in response between different patient’s subgroups?

In addi t ion to des ign ing the studies and answer ing and quantifying study design-related questions, dif ferent study designs can be com-pared to assess how well they answer the questions posed and deliver the biggest bang for the buck. In other words, given that a high proportion of late-stage clinical studies fail, models can be used to simulate clinical study outcomes and to quantify the prob-ability of success and facilitate the “go/no-go” decision-making process.

There is no one-size-fits-all model or simulation; the model is shaped by the questions that need answering, the available data, and mathematical and statistical descrip-tions of physiological and pharmacological processes as well as company preferences (see Figure 1 and Figure 2). Nonetheless, there are qualified and validated generalized

and comprehensive software packages that allow the use of existing clinical study data and other prior knowledge to build models and simulations that facilitate the exploration of a wide variety of scenarios inherent to the design clinical of studies. Simulo, developed by SGS Exprimo in partner-ship with Roche, is one of these clinical trial simulator softwares with a user-friendly interface, to provide a clear but solid simulation framework. Simulo offers a platform on which mathematical/statistical drug-disease models easily can be implemented and efficiently run, based on pre-programmed modules.

•  Key motivation: High proportion of clinical trials fails in late stage ⇒ Large expenditure of money and time

•  Idea: Simulate study outcome upfront ⇒ Evaluate P(“success”)

Probability of “success”

PK PK/PD Patient population

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Prior knowledge

Trial model (clinical endpoint, trt duration, active ctrl, sample size, …)

Performing clinical trial simulations

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ProtocolA Yes 8.2mg/L

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ProtocolB 71% 9.0(8.3-9.6)

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21

Figure 2: Proposed inputs for a clinical trial simulation model.

Figure 1: Model for clinical trial simulations.

CLINICAL TRIAL SIMULATION

Reducing the Risk that Studies Will FailThe business case for modeling and simulation in drug devel-opment has been described in published studies. In addition to optimizing study designs (i.e., study size and dose selection), these models can be used to quantify the study’s probability of success and facilitate go/no-go decisions.

Modeling can be used to quantify the probability that the drug product can deliver the needed effect or give a higher effect than what is on the market, with acceptable side effects, in addition to quantifying how much more effect a drug has over its main competitor at the highest financially viable dose.

For example, the probability of success can be described as the probability to demonstrate non-inferiority of one regimen over another where the two treat-ment regimens are expected to be similar. In this paradigm, the 90% confidence interval for a given test statistic (e.g., hazard ratio of two treatment regimens) were generated and compared to a predefined “margin” value.

Generally, non-inferiority is demonstrated if the test statistic generated by the simulation is less than the margin value and the probability of success is the percentage of the simulated trials that show non-inferiority. Using these results, the prob-ability of success of different combinations of margins and numbers of patients (i.e., sample size) can be simulated (Figure 3).

Improving, Optimizing, and Justifying Clinical Trial Study DesignsIn addition to exploring various what-if scenarios, simulations also enable the exploration and iterative fine-tuning of clinical study designs. The “power” of a study is often a key consid-eration in study designs. For example, simulation showed that increasing study duration from six weeks to 12 weeks increased the power of the study from less than 70% to more than 80% (see Figure 4).

Us ing th is same mode l, simulation also showed that by

excluding patients with mild disease (changing the baseline), the power of the study was further improved. Another study simulated the effect of treatment with an Alzheimer’s drug compared to treatment with a placebo. Although the data showed that the drug did slow the progression of the disease and that the patients receiving placebo got no benefit, mod-eling allowed the simulation of a “delayed start” study design in which all study participants received the active drug after one year of treatment. The results showed that although 250 patients gave an 80% chance of having an effect, more than 600 patients would be needed in the delayed-start design to

•  For M=1.6: N=840 ⇒ Psucc=60%, however N=1000 ⇒ Psucc=75%

•  For Psucc=80%: if M=2.7 ⇒ N=390, but if M=2.15 ⇒ N=600

•  For N=840: M=2.2 ⇒ Psucc=90%, but for more conservative M=1.6 ⇒ Psucc=60%

•  For Psucc=90%: if N=1000 ⇒ M=1.9, but if N=600 ⇒ M=2.55

P(success) vs NI margin by sample size at randomization

Different study durations were evaluated by simulation Increasing the duration to 12 weeks increased the power to >80%

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Figure 4: Different study durations evaluated by simulation.

Figure 3: Estimating the probability of success using different margin values and number of patients for a hypothetical non-inferiority trial (endpoint: hazard ratio).

CLINICAL TRIAL SIMULATION

show the same 80% chance of having an effect.

Guiding dose selectionMechanistic models translating PK and PD data from

animal to man and further refined using PK and PD data from man enabled the simulation of drug exposure for a Phase 1 first-in-man study. The simulation showed that the pharmacokinetics were non-linear and that increasing doses Mechanistic translation of PK and PD from animal to man

allows for Phase 1 dose selection and design

Target-mediated drug disposition concentration-time profile simulations

Uncertainty in scaling can be captured in initial model

0.01mg/kg

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Figure 5: Mechanistic translation of PK and PD from animal to man.

Example of pediatric dosing to match adult exposure

A large number of dose adjustment regimes were explored, until one was found that matches exposure in children to exposure in adults

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Figure 6: Comparison of exposures in children with and without dose adjustment.

CLINICAL TRIAL SIMULATION

of drug could extend exposure to over 60 days. In addition to answering questions about the starting dose, this simula-tion answered questions related to study duration (i.e., what should the frequency of measuring serum concentrations be and how long patients should be followed or monitored) (see Figure 5).

For clinical studies evaluating comparator drugs, the likeli-hood of different doses of the study drug being better than the comparator drug in achieving a desired effect or having an impact on efficacy can be simulated. Studies like these can guide dosing for Phase 3 studies as well as choosing comparator drugs.

PK/PD models can be also used to support dose selection and study design of pediatric clinical trials. For example, a model based on exposures in adults could be used to explore a large number of dose adjustment regimens to simulate expo-sures in children as a function of body weight or body surface area and find a regimen that matched the adult exposure (see Figure 6).

SummaryRobust modeling and simulation techniques have proven their worth for exploring adult and pediatric dosing regimens,

comparing similar drugs against one another, the effect of single and combination treatments, study size, or number of patients needed to achieve a defined power, as well as providing answers to many other clinical trial design-related questions. These simulations also help quantify the probability of success that a particular clinical trial study design will pro-vide the needed information. Looking to the future, the conflu-ence of the mandated FDA investment in new ex silico drug development tools, the EU’s Innovative Medicines Initiative, and the use of modeling and simulation tools by sponsors will be increasingly used to support regulatory approval for the investigational use of and licensure of new drugs.

References1. S. Gottlieb, “How FDA Plans to Help Consumers

Capitalize on Advances in Science,” July 7, 2017, https://blogs.fda.gov/fdavoice/index.php/2017/07/how-fda-plans-to-help-consumers-capitalize-on-advances-in-science, accessed Sept. 29, 2017.

2. S.R.B. Allerheiligen, Clin. Pharm. Ther. Vol. 96, 413–415 (2014).

To contact the authors: clinicalresearch@sgs.comwww.sgs.com\cro or www.exprimo.com

SGS is a Life Sciences CRO offering clinical research and bio/analytical testing across Europe and Americas with a focus on early phase trials and biometrics. SGS provides Phase I to IV trial services including medical writing, data manage-ment, biostatistics, PK/PD Modeling & simulation, pharmacovigilance and regulatory consultancy. A side of the SGS leading European biometric group of 250 people, SGS Exprimo focuses on the application of population PK, advanced PK/PD and drug-disease modelling & simulation to help decision making in drug development.

This executive summary is based on material presented in a webcast that can be viewed on demand here.