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Table of contents

Each class of product may undergo different types of preclinical research. For instance, drugs may undergo pharmacodynamics what the drug does to the body PD , pharmacokinetics what the body does to the drug PK , ADME , and toxicology testing. This data allows researchers to allometrically estimate a safe starting dose of the drug for clinical trials in humans. Medical devices that do not have drug attached will not undergo these additional tests and may go directly to good laboratory practices GLP testing for safety of the device and its components.

Some medical devices will also undergo biocompatibility testing which helps to show whether a component of the device or all components are sustainable in a living model. Typically, both in vitro and in vivo tests will be performed. Studies of a drug's toxicity include which organs are targeted by that drug, as well as if there are any long-term carcinogenic effects or toxic effects on mammalian reproduction. The information collected from these studies is vital so that safe human testing can begin. Typically, in drug development studies animal testing involves two species.

The most commonly used models are murine and canine , although primate and porcine are also used. The choice of species is based on which will give the best correlation to human trials. Differences in the gut , enzyme activity , circulatory system , or other considerations make certain models more appropriate based on the dosage form , site of activity, or noxious metabolites.

For example, canines may not be good models for solid oral dosage forms because the characteristic carnivore intestine is underdeveloped compared to the omnivore's, and gastric emptying rates are increased. Also, rodents can not act as models for antibiotic drugs because the resulting alteration to their intestinal flora causes significant adverse effects.

Depending on a drug's functional groups, it may be metabolized in similar or different ways between species, which will affect both efficacy and toxicology. Medical device studies also use this basic premise. Most studies are performed in larger species such as dogs, pigs and sheep which allow for testing in a similar sized model as that of a human. In addition, some species are used for similarity in specific organs or organ system physiology swine for dermatological and coronary stent studies; goats for mammary implant studies; dogs for gastric and cancer studies; etc.

Importantly, the regulatory guidelines of FDA , EMA , and other similar international and regional authorities usually require safety testing in at least two mammalian species, including one non-rodent species, prior to human trials authorization.

Animal testing in the research-based pharmaceutical industry has been reduced in recent years both for ethical and cost reasons. However, most research will still involve animal based testing for the need of similarity in anatomy and physiology that is required for diverse product development. We showed how decision-theoretic VOI analysis suggests a more flexible approach with both type I error rate and power or equivalently trial sample size depending on the size of the future population for whom the treatment under investigation is intended.

Taking a more general viewpoint, we have shown that for a wide range of distributions, including those for continuous, binary or count responses, and gain function forms, the optimal trial sample size is proportional to the square root of the population size, with the constant of proportionality depending on the gain function form and prior distribution of the parameters of the distribution of the data [ 13 ].

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In each case we outline in detail the reasonable choice of parameters for the different approaches and calculate sample sizes accordingly. This work illustrates the influence of the input parameters in the different approaches and we recommend investigating different sample size approaches before deciding finally on the sample size. We have also developed decision-theoretic methods for the simultaneous design of a series of trials in a small fixed population.

Use of the methodology has been illustrated through retrospective application in an example in small orthopaedic surgery trials [ 15 ]. Further work to extend the models developed is ongoing. In particular, we are exploring the optimal design of multistage trials, settings in which the disease prevalence is considered unknown, with information obtained from the rate of recruitment to the trial itself, and designs that are optimal for more different stakeholders such as regulatory authorities and industrial sponsors. The development of targeted therapies that act on certain molecular mechanisms of diseases requires specific trial design and analytical methods.

We performed a literature search to summarize the currently available methodology for the identification and confirmation of targeted subgroups in clinical trials [ 16 ].

How artificial intelligence is changing drug discovery

In total 86 scientific articles proposing relevant methods were identified that were classified as confirmatory, exploratory or applicable in a confirmatory as well as exploratory settings. The review identified a wide range of trial designs, including fixed sample, group sequential, and several types of adaptive designs. In our work we have considered designs where subgroups are defined based on a continuous biomarker and several thresholds are considered to define the subgroup. We derived confirmatory testing procedures that control false positive rates if several thresholds are under consideration [ 17 ] and show that the type I error rate of earlier proposed testing procedures based on group sequential rejection boundaries may be inflated if the biomarker has a prognostic effect e.

Consequently, we propose improved hypotheses testing approaches based on regression models and combination tests that robustly control the familywise error rate. We also investigated adaptive enrichment designs. In these two-stage designs, in the first stage patients are recruited from the full population. Following an interim analysis, based on the interim data, the design of the second stage may be modified. We provided a comprehensive description of the statistical methodologies for confirmative adaptive designs with multiple objectives and their application in adaptive two-stage enrichment designs [ 19 , 20 ].

For the special case of adaptive designs with a survival endpoint, hypothesis tests were developed that allow for early rejection of the null hypothesis at an interim analysis. This work generalizes earlier adaptive procedures that control the familywise type I error rate in the strong sense but have limitations in that they either cannot use information from surrogate endpoints for adaptive decision making or do not allow early rejections at an interim analysis.

To guide the design of clinical trials for the development of targeted therapies, working together with the IDeAl project, we developed a decision-theoretic framework to optimize single stage and adaptive two-stage designs [ 21 — 23 ]. Here we assume that the utility of the sponsor is the net present value of a trial, while for society it is the expected health benefit adjusted for the trial cost.

In the planning phase, expected utilities for different trial designs and different utility functions are computed based on Bayesian prior distributions for the effect sizes in the subgroup and the full population.

When classic methods don’t cut it

Then optimal trial designs are identified that maximize these expected utilities by optimizing the sample size, the multiple testing procedure and the type of the design. The considered types of trials include classical designs, where no biomarker information is used and only the full population is tested, enrichment designs, where only biomarker positive patients are included, stratified designs, where patients from the full population are included and the treatment effect is tested in the subgroup and the full population, and partial enrichment designs, where the prevalence of the subgroup in the trial is a design parameter that can be chosen to maximize the expected utility.

We found that the optimal trial designs depend on the prevalence of the subgroup, the strength of the prior evidence that the treatment effect varies across subgroups, and on the cost of biomarker development and determination. Furthermore, we observe that optimal designs for the sponsor and the societal view differ.

Trials optimized under the sponsor view tend to have smaller sample sizes and are conducted in the full population even in settings where there is substantial prior evidence that the treatment is effective in the subpopulation only.


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This is due to the fact that the variability of treatment effect estimates means a treatment might appear effective in a subpopulation and bring a gain for the sponsor even if it is not effective and has no benefit for patients. We also extended the work to consider adaptive two-stage enrichment designs. We showed that adaptive enrichment designs can lead to a higher expected utility than single stage designs, especially in settings where there is high uncertainty if the treatment is effective only in a subgroup.

As for single stage designs, we observe differences in the optimized designs if trials are optimized under the sponsor or the societal perspective. An important advantage of adaptive designs compared to single stage designs is their increased robustness with regard to a misspecification of the planning assumptions. Optimal adaption rules of adaptive enrichment designs, optimized for a sponsor left graph and a societal perspective right graph.

Depending on the observed standardized treatment effects in the biomarker positive plotted on the x-axes and negative plotted on the y-axes population, the graph indicates the optimal second stage design option: futility stop white area , enrichment design, recruiting biomarker positive patients only red area , or partially enriched design grey area. In addition, the second stage sample sizes are optimized not shown in the graph. The optimisation is based on an a priori distribution on the effect sizes corresponding to the assumption that the treatment effect is either independent of the biomarker or that it is larger or only present in biomarker positive patients.

See Ondra et al. Early phase dose-finding studies aim to obtain reliable information on an appropriate dose for use in further clinical trials. The designs used have generally relied primarily on observed toxicity data [ 24 ]. We proposed and compared methods to incorporate PK measures in the dose allocation process during phase I clinical trials. PK observations were incorporated in a number of different ways; as a covariate, as a dependent variable or in a hierarchical modelling approach.

We conducted a large simulation study which showed that adding PK measurements as a covariate alone does not improve the efficiency of dose-finding trials either in terms of reducing the number of observed toxicities or improving the probability of correct dose selection. However, incorporating PK measures through a hierarchical model leads to better estimation of the dose-toxicity curve whilst maintaining the performance in terms of dose selection compared to dose-finding designs that do not incorporate PK information [ 25 ].

We developed an R package, dfpk , to provide a tool for physicians and statisticians involved in such clinical trials implementing the new method [ 26 ]. In the trial, 3 primary outcomes were considered: efficacy and two types of toxicity that occur at the same time but can be measured earlier or later in time. The primary outcomes were modelled using a Bayesian approach with a logistic model for efficacy and a weighted likelihood with pseudo-outcomes for the two toxicities taking into account the correlation between the outcomes.

This trial has received ethical committee approval and recruitment started in October We have also focused on the development of possible extrapolation methods using information from studies in adults in the design of clinical trials in pediatrics. A unified approach for extrapolation and bridging adult information in early phase dose-finding studies was proposed. Using this approach we have investigated the choice of the dose range and calibration of prior density parameters of the dose-finding models for clinical trials involving children.

The method uses adult observations, such as PK data, toxicity and efficacy. A large simulation study has shown that our method is robust and gives good performance in terms of dose selection [ 27 , 28 ]. An R package, dfped , was developed to enable implementation of the new method [ 29 ].