Interpreting clinical trial outcomes complicated by placebo response with an assessment of false-negative and true-negative clinical trials in depression using propensity-weighting.

The objective of this study was to evaluate the performances of the propensity score weighted (PSW) methodology in a post-hoc re-analysis of a failed and a negative RCTs in depressive disorders. The conventional study designs, randomizations, and statistical approaches do not account for the baseline distribution of major non-specific prognostic and confounding factors such as the individual propensity to show a placebo effect (PE). Therefore, the conventional analysis approaches implicitly assume that the baseline PE is the same for all subjects in the trial even if this assumption is not supported by our knowledge on the impact of PE on the estimated treatment effect (TE). The consequence of this assumption is an inflation of false negative results (type II error) in presence of a high proportion of subjects with high PE and an inflation of false positive (type I error) in presence of a high proportion of subjects with low PE value. Differently from conventional approaches, the inverse of the PE probability was used as weight in the mixed-effects analysis to assess TE in the PSW analysis. The results of this analysis indicated an enhanced signal of drug response in a failed trial and confirmed the absence of drug effect in a negative trial. This approach can be considered as a reference prospective or post-hoc analysis approach that minimize the risk of inflating either type I or type II error in contrast to what happens in the analyses of RCT studies conducted with the conventional statistical methodology.

A new method for analyzing clinical trials in depression based on individual propensity to respond to placebo estimated using artificial intelligence.

One of the major reasons for trial failures in major depressive disorders (MDD) is the presence of unpredictable levels of placebo response as the individual baseline propensity to respond to placebo is not adequately controlled by the current randomization and statistical methodologies. The individual propensity to respond to any treatment or intervention assessed at baseline was considered as a major non-specific prognostic and confounding effect. The objective of this paper was to apply the propensity score methodology to control for potential imbalance at baseline in the propensity to respond to placebo in clinical trials in MDD. Individual propensity was estimated using artificial intelligence (AI) applied to observations collected in two pre-randomization occasions. Cases study are presented using data from two randomized, placebo-controlled trials to evaluate the efficacy of paroxetine in MDD. AI models were used to estimate the individual propensity probability to show a treatment non-specific placebo effect. The inverse of the estimated probability was used as weight in the mixed-effects analysis to assess treatment effect. The comparison of the results obtained with and without propensity weight indicated that the weighted analysis provided an estimate of treatment effect and effect size significantly larger than the conventional analysis.

Artificial intelligence approach for the analysis of placebo-controlled clinical trials in major depressive disorders accounting for individual propensity to respond to placebo.

Treatment effect in clinical trials for major depressive disorders (RCT) can be viewed as the resultant of treatment specific and non-specific effects. Baseline individual propensity to respond non-specifically to any treatment or intervention can be considered as a major non-specific confounding effect. The greater is the baseline propensity, the lower will be the chance to detect any treatment-specific effect. The statistical methodologies currently applied for analyzing RCTs doesn't account for potential unbalance in the allocation of subjects to treatment arms due to heterogenous distributions of propensity. Hence, the groups to be compared may be imbalanced, and thus incomparable. Propensity weighting methodology was used to reduce baseline imbalances between arms. A randomized, double-blind, placebo controlled, three arms, parallel group, 8-week, fixed-dose study to evaluate efficacy of paroxetine CR 12.5 and 25 mg/day is presented as a cases study. An artificial intelligence model was developed to predict placebo response at week 8 in subjects assigned to placebo arm using changes from screening to baseline of individual Hamilton Depression Rating Scale items. This model was used to predict the probability to respond to placebo in each subject. The inverse of the probability was used as weight in the mixed-effects model applied to assess treatment effect. The analysis with and without propensity weight indicated that the weighted analysis provided an estimate of treatment effect and effect-size about twice larger than the non-weighted analysis. Propensity weighting provides an unbiased strategy to account for heterogeneous and uncontrolled placebo effect making patients' data comparable across treatment arms.

Convolution-based approach for modeling the paliperidone extended release and Long-Acting Injectable (LAI) PK of once-, and three-monthly products administration and for optimizing the development of new LAI products.

The aim of this paper was to develop a convolution-based modeling approach for describing the paliperidone PK resulting from the administration of extended-release once-a-day oral dose, and once- and three monthly long-acting injectable products and to compare the performances of this approach to the traditional modeling strategy. The results of the analyses indicated that the traditional and convolution-based models showed comparable performances in the characterization of the paliperidone PK. However, the convolution-based approach showed several appealing features that justify the choice of this modeling as a preferred tool for modeling Long Acting Injectable (LAI) products and for deploying an effective model-informed drug development process. In particular, the convolution-based modeling can (a) facilitate the development of in vitro/in vivo correlation, (b) be used to identify formulations with optimal in vivo release properties, and (c) be used for optimizing the clinical benefit of a treatment by supporting the implementation of integrated models connecting in vitro and in vivo drug release, in vivo drug release to PK, and PK to PD and biomarker endpoints. A case study was presented to illustrate the benefits and the flexibility of the convolution-based modeling outcomes. The model was used to evaluate the in vivo drug release properties associated with a hypothetical once-a-year administration of a LAI product with the assumption that the expected paliperidone exposure during a 3-year treatment overlays the exposure expected after repeated administrations of a 3-month LAI product.

Modeling Complex Pharmacokinetics of Long-Acting Injectable Products Using Convolution-Based Models With Nonparametric Input Functions.

The interest in the development and the therapeutic use of long-acting injectable (LAI) products for chronic or long-term treatments has grown exponentially. The complexity and the multiphase drug release process represent serious issues for an effective modeling of the PK properties of LAI products. The objective of this article is to show how convolution-based models with piecewise-linear approximation of the nonlinear drug release function can provide an enhanced modeling tool for (1) characterizing the complex PK profiles of LAI formulations with completely different drug release properties, and (2) addressing key questions supporting the optimal development of LAI products by simulating the PK time course resulting from different dosing strategies. Convolution-based modeling and simulation were implemented in NONMEM, and 3 case studies were presented to assess the performances of this new modeling approach using PK data of LAI products developed using different technologies and administered using different routes: microsphere technology and aqueous nanosuspension intramuscularly administered and biodegradable polymer subcutaneously administered. The performance of the convolution-based modeling approach was compared with the performance of conventional parametric models using a reference data set on theophylline. The results of the comparison indicated that the nonparametric input function provided a more accurate description of the data either in terms of global measure of goodness of fit (ie, Akaike information criterion and Bayesian information criterion) or in terms of performance of the fitted model (ie, the percent prediction error on Cmax and AUC0-t ).

Model based approach for estimating the dosage regimen of indomethacin a potential antiviral treatment of patients infected with SARS CoV-2.

To face SARS-CoV-2 pandemic various attempts are made to identify potential effective treatments by repurposing available drugs. Among them, indomethacin, an anti-inflammatory drug, was shown to have potent in-vitro antiviral properties on human SARS-CoV-1, canine CCoV, and more recently on human SARS-CoV-2 at low micromolar range. Our objective was to show that indomethacin could be considered as a promising candidate for the treatment of SARS-CoV-2 and to provide criteria for comparing benefits of alternative dosage regimens using a model-based approach. A multi-stage model-based approach was developed to characterize % of recovery and viral load in CCoV-infected dogs, to estimate the PK of indomethacin in dog and human using published data after administration of immediate (IR) and sustained-release (SR) formulations, and to estimate the expected antiviral activity as a function of different assumptions on the effective exposure in human. Different dosage regimens were evaluated for IR formulation (25 mg and 50 mg three-times-a-day, and 25 mg four-times-a-day), and SR formulation (75 mg once and twice-a-day). The best performing dosing regimens were: 50 mg three-times-a-day for the IR formulation, and 75 mg twice-a-day for the SR formulation. The treatment with the SR formulation at the dose of 75 mg twice-a-day is expected to achieve a complete response in three days for the treatment in patients infected by the SARS-CoV-2 coronavirus. These results suggest that indomethacin could be considered as a promising candidate for the treatment of SARS-CoV-2 whose potential therapeutic effect need to be further assessed in a prospective clinical trial.

Comparison of Alternative Population Modeling Approaches for Implementing a Level A IVIVC and for Assessing the Time-Scaling Factor Using Deconvolution and Convolution-Based Methods.

Different approaches based on deconvolution and convolution analyses have been proposed to establish IVIVC. A new implementation of the convolution-based model was used to evaluate the time-scaled IVIVC using the convolution (method 1) and the deconvolution-based (method 2) approaches. With the deconvolution-based approach, time-scaling was detected and estimated using Levy's plots while with the convolution-based approach, time-scaling was directly determined by a time-scaling sub-model of the convolution integral model by nonlinear regression. The objectives of this study were (i) to show how time-scaled deconvolution and convolution-based approaches can be implemented using population modeling approach using standard nonlinear mixed-effect modeling software such as NONMEM and R, and (ii) to compare the performances of the two methods for assessing IVIVC using complex in vivo drug release process. The impact of different PK scenarios (linear and nonlinear PK disposition models, and increasing levels of inter-individual variability (IIV) on in vivo drug release process) was considered. The performances of the methods were assessed by computing the prediction error (%PE) on Cmax, AUC, and partial AUC values. The mean %PE values estimated with the two methods were compliant with the IVIVC validation criteria. However, different from convolution-based, deconvolution-based approach showed that (i) the increase of IIV on in vivo drug release significantly affects the maximal %PE values of Cmax leading to failure of IVIVC validation, and (ii) larger %PE values for Cmax were associated to complex nonlinear PK disposition models. These results suggest that convolution-based approach could be considered at preferred approach for assessing time-scaled IVIVC.

Deconvolution Analysis by Non-linear Regression Using a Convolution-Based Model: Comparison of Nonparametric and Parametric Approaches.

The convolution-based modeling approach has been shown to be flexible and easy to implement for performing a deconvolution analysis and for assessing in vitro/in vivo correlation using non-linear regression and a pre-specified model describing the in vivo drug absorption. A generalization of this method has been developed using a nonparametric description of the in vivo drug absorption process in replacement of a model-based definition. A comparison of the parametric and nonparametric deconvolution and convolution analyses was conducted on the pharmacokinetic (PK) data observed in four published studies after the administration of an extended-release formulation of methylphenidate at the dose of 18 mg. All the analyses were conducted using a conventional non-linear regression software (NONMEM). The results of the deconvolution analysis indicated that the parametric and nonparametric approaches performed similarly. The parametric approach described the input function using a double Weibull equation (6 parameters) while the nonparametric approach described the input function using a piecewise approximation (12-13 parameters). The validation of the results of the deconvolution analysis was conducted by comparing observed and predicted PK concentrations by the convolution analysis. The performance of the parametric and nonparametric approaches for assessing deconvolution was evaluated using the Akaike and the Bayesian information criteria. These criteria indicated that, despite the similar results obtained with the two approaches, the nonparametric approach provided better results. In conclusion, these results indicated that the nonparametric approach should be considered as the preferred approach for conducting a deconvolution analysis.

Model-Informed Approach to Assess the Treatment Effect Conditional to the Level of Placebo Response.

One of the most important reasons for failure of placebo-controlled randomized controlled clinical trials (RCTs) is the lack of appropriate methodologies for detecting treatment effect (TE; difference between placebo and active treatment response) in the presence of excessively low/high levels of placebo response. Although, the higher the level of placebo response in a trial, the lower the apparent detectable TE. TE is usually estimated in a conventional analysis of an RCT as an "apparent" TE value conditional to the level of placebo response in that RCT. A model-informed methodology is proposed to establish a relationship between level of placebo response and TE. This relationship is used to estimate the "typical" TE associated with a "typical" level of placebo response, irrespective of the level of placebo response observed. The approach can be valuable for providing a reliable estimate of TE, for conducting risk/benefit analysis, and for determining dosage recommendations.

A General Framework for Assessing In vitro/In vivo Correlation as a Tool for Maximizing the Benefit-Risk Ratio of a Treatment Using a Convolution-Based Modeling Approach.

The net benefit of a treatment can be defined by the relationship between clinical improvement and risk of adverse events: the benefit-risk ratio. The optimization of the benefit-risk ratio can be achieved by identifying the most adequate dose (and/or dosage regimen) jointly with the best-performing in vivo release properties of a drug. A general in silico tool is presented for identifying the dose, the in vitro and the in vivo release properties that maximize the benefit-risk ratio using convolution-based modeling, an exposure-response model, and a surface response analysis. A case study is presented to illustrate how the benefit-risk ratio of methylphenidate for the treatment of attention deficit hyperactivity disorder can be maximized using the proposed strategy. The results of the analysis identified the characteristics of an optimized dose and in vitro/in vivo release suitable to provide a sustained clinical response with respect to the conventional dosage regimen and formulations.

Is It Time for Going Beyond the P-Value Paradigm With the Estimation of the Probability of Clinical Benefit as a Criterion for Assessing the Outcomes of a Clinical Trial? A Case Study in Patients With Major Depressive Disorder.

The conventional statistical methodologies for evaluating treatment effect are based on hypothesis testing (P-value). Alternative measurements of treatment effect have been proposed for anti-infective treatments using the probability of target attainment. A general framework is proposed to extend this methodology to other therapeutic areas. A disease trial model is used for estimating the probability of reaching a treatment effect associated with relevant clinical benefits, in complement to the evaluation of the probability of rejecting the null hypothesis. A case study is presented in depression, where disease status is evaluated using bounded clinical scores (Hamilton Depression Rating Scale), and detectable treatment effect is inversely proportional to placebo response. The ß-regression approach is used to model Hamilton scale scores, and a placebo-related criterion is proposed for determining the clinical benefit. The probability of reaching a clinical benefit represents a reliable criterion for replacing the P-value paradigm in the assessment of the outcomes of clinical trials.

Liquid chromatography-electrospray ionization-tandem mass spectrometry method for quantitative estimation of new imiqualine leads with potent anticancer activities in rat and mouse plasma. Application to a pharmacokinetic study in mice.

Imidazoquinoxaline derivatives (imiqualines) are a new series of anticancer compounds. Two lead compounds (EAPB0203 and EAPB0503) with remarkable in vitro and in vivo activity on melanoma and T-cell lymphomas have been previously identified. The modulation of the chemical structure of the most active compound, EAPB0503, has led to the synthesis of two compounds, EAPB02302 and EAPB02303, 7 and 40 times more active than EAPB0503 against A375 human melanoma cancer cell line, respectively. The aim of this study was to develop and validate a sensitive and accurate liquid chromatography-electrospray ionization-tandem mass spectrometry method to simultaneously quantify EAPB02303 and its potential active metabolite, EAPB02302, in rat and mouse plasma. Analytes were detected in multiple reaction monitoring acquisition mode using an electrospray ionization detector in positive ion mode. Following a liquid-liquid extraction with ethyl acetate, analytes and internal standard were separated by HPLC reversed-phase on a C18 RP18 Nucleoshell column (2.7µm, 4.6×100mm). The method was validated according to FDA and EMA Bioanalytical Method Validation guidelines. The robustness of the method was assessed by introducing small variations in nine nominal analytical parameters. Statistical interpretation was performed by mean of the Student's t-test. Standard curves were generated via unweighted quadratic regression of calibrators (EAPB02303: 1.95-1000ng/mL, EAPB02302: 7.81-1000ng/mL in rat plasma; EAPB02303: 0.98-1000ng/mL, EAPB02302: 1.95-1000ng/mL in mouse plasma). From the analysis of QC samples, intra- and inter-assay precision and accuracy studies demonstrated %R.S.Ds. <12.5% and percent deviation from nominal concentration <7%. Matrix effects (mean matrix factors from 91.8-108.5% in rat plasma; and from 90.4-102.4% in mouse plasma) and stability assays (recoveries >87%) were acceptable and in accordance with the guidelines. No quantifiable carryover effect was observed. The LLOQs were 1.95ng/mL for EAPB02303 and 7.81ng/mL for EAPB02302 in rat plasma, and 0.98ng/mL and 1.95ng/mL for the two compounds in mouse plasma, respectively. This method was successfully implemented to support a mouse pharmacokinetic study following a single intraperitoneal administration of EAPB02303 in male C57Bl/6 mice. The obtained pharmacokinetic parameters of EAPB02303 would be useful to optimize the dosing and the rhythm of administration for subsequent preclinical in vivo activity studies.

High Accumulation and In Vivo Recycling of the New Antimalarial Albitiazolium Lead to Rapid Parasite Death.

Albitiazolium is the lead compound of bisthiazolium choline analogues and exerts powerful in vitro and in vivo antimalarial activities. Here we provide new insight into the fate of albitiazolium in vivo in mice and how it exerts its pharmacological activity. We show that the drug exhibits rapid and potent activity and has very favorable pharmacokinetic and pharmacodynamic properties. Pharmacokinetic studies in Plasmodium vinckei-infected mice indicated that albitiazolium rapidly and specifically accumulates to a great extent (cellular accumulation ratio, >150) in infected erythrocytes. Unexpectedly, plasma concentrations and the area under concentration-time curves increased by 15% and 69% when mice were infected at 0.9% and 8.9% parasitemia, respectively. Albitiazolium that had accumulated in infected erythrocytes and in the spleen was released into the plasma, where it was then available for another round of pharmacological activity. This recycling of the accumulated drug, after the rupture of the infected erythrocytes, likely extends its pharmacological effect. We also established a new viability assay in the P. vinckei-infected mouse model to discriminate between fast- and slow-acting antimalarials. We found that albitiazolium impaired parasite viability in less than 6 and 3 h at the ring and late stages, respectively, while parasite morphology was affected more belatedly. This highlights that viability and morphology are two parameters that can be differentially affected by a drug treatment, an element that should be taken into account when screening new antimalarial drugs.

Development and validation of a liquid chromatography-electrospray ionization-tandem mass spectrometry method for the determination of urolithin C in rat plasma and its application to a pharmacokinetic study.

Urolithins are microflora human metabolites of dietary ellagic acid derivatives. There is now a growing interest in the biological activities of these compounds. Several studies suggest that urolithins have potential antioxidant, anti-inflammatory, anticancer and anti-glycative activities. Recently, our group investigated the role of urolithins as potential anti-diabetic treatments; among the four urolithins, urolithin C was the most promising compound. The purpose of this paper was to develop a rapid, sensitive and specific liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS) method for the determination of urolithin C in rat plasma. To date, no method is reported for the quantification of urolithin C in any of the matrices. Plasma samples were extracted with ethyl acetate. Urolithin D was selected as the internal standard. The separation was carried out on a C18 Kinetex EVO column (2.1mm×150mm, 2.6µm) using a mobile phase of acetonitrile-1% aqueous formic acid solution (30:70, v/v). A triple quadrupole mass spectrometer in the negative ion mode was used for the determination of the target analyte. The monitored ion transitions were m/z 243→187 for urolithin C and m/z 259→213 for the internal standard. The calibration curve range was 4.95-1085µg/L (r2>0.994). The intra- and inter-day precisions were less than 10%; accuracies ranged from 96.6 to 109%. The mean extraction recovery of urolithins C and D was greater than 91%. No significant matrix effects and no carryover effects were observed. Small changes in LC-ESI-MS/MS conditions did not have significant effect on the determination of urolithin C. Stability tests under various conditions were also investigated. This highly specific and sensitive method was used to analyze samples collected during preclinical pharmacokinetic studies in rats. Glucuronyl and sulfate conjugates of urolithin C were the main metabolites detected in plasma.

Response Surface Analysis and Nonlinear Optimization Algorithm for Maximization of Clinical Drug Performance: Application to Extended-Release and Long-Acting Injectable Paliperidone.

Model-based approach is recognized as a tool to make drug development more productive and to better support regulatory and therapeutic decisions. The objective of this study was to develop a novel model-based methodology based on the response surface analysis and a nonlinear optimizer algorithm to maximize the clinical performances of drug treatments. The treatment response was described using a drug-disease model accounting for multiple components such as the dosage regimen, the pharmacokinetic characteristics of a drug (including the mechanism and the rate of drug delivery), and the exposure-response relationship. Then, the clinical benefit of a treatment was defined as a function of the diseases and the clinical endpoints and was estimated as a function of the target pharmacodynamic endpoints used to evaluate the treatment effect. A case study is presented to illustrate how the treatment performances of paliperidone extended release (ER) and paliperidone long-acting injectable (LAI) can be improved. A convolution-based approach was used to characterize the pharmacokinetics of ER and LAI paliperidone. The drug delivery properties and the dosage regimen maximizing the clinical benefit (defined as the target level of D2 receptor occupancy) were estimated using a nonlinear optimizer. The results of the analysis indicated that a substantial improvement in clinical benefit (from 15% to 27% for the optimization of the in vivo release and from ∼30% to ∼70% for the optimization of dosage regimen) was obtained when optimal strategies were deployed either for optimizing the in vivo drug delivery properties of ER formulations or for optimizing the dosage regimen of LAI formulations.