Mahesh N. Samtani

The ADAS-Cog revisited: novel composite scales based on ADAS-Cog to improve efficiency in MCI and early AD trials.

The Alzheimers Disease Assessment Scale‐Cognitive (ADAS‐Cog) has been used widely as a cognitive end point in Alzheimers Disease (AD) clinical trials. Efforts to treat AD pathology at earlier stages have also used ADAS‐Cog, but failure in these trials can be difficult to interpret because the scale has well‐known ceiling effects that limit its use in mild cognitive impairment (MCI) and early AD. A wealth of data exists in ADAS‐Cog from both historical trials and contemporary longitudinal natural history studies that can provide insights about parts of the scale that may be better suited for MCI and early AD trials.

Quantifying the Pathophysiological Timeline of Alzheimer's Disease

Hypothetical models of AD progression typically relate clinical stages of AD to sequential changes in CSF biomarkers, imaging, and cognition. However, quantifying the continuous trajectories proposed by these models over time is difficult because of the difficulty in relating the dynamics of different biomarkers during a clinical trial that is significantly shorter than the duration of the disease. We seek to show that through proper synchronization, it is possible to de-convolve these trends and quantify the periods of time associated with different pathophysiological changes associated with Alzheimers disease (AD). We developed a model that replicated the observed progression of ADAS-Cog 13 scores and used this as a more precise estimate of disease-duration and thus pathologic stage. We then synchronized cerebrospinal fluid (CSF) and imaging biomarkers according to our new disease timeline. By de-convolving disease progression via ADAS-Cog 13, we were able to confirm the predictions of previous hypothetical models of disease progression as well as establish concrete timelines for different pathobiological events. Specifically, our work supports a sequential pattern of biomarker changes in AD in which reduction in CSF Aβ(42) and brain atrophy precede the increases in CSF tau and phospho-tau.

Simple pharmacometric tools for oral anti‐diabetic drug development: competitive landscape for oral non‐insulin therapies in type 2 diabetes

The objectives were to develop a translational model that will help select doses for Phase‐3 trials based on abbreviated Phase‐2 trials and understand the competitive landscape for oral anti‐diabetics based on efficacy, tolerability and ability to slow disease progression. Data for eight anti‐diabetics with temporal profiles for fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) from 12 publications were digitized. The monotherapy data consisted of FPG and HbA1c profiles that were modeled using a transit compartment model. Evaluation of the competitive landscape utilized HbA1c literature data for 11 drugs. For the safety metric, tolerability issues with anti‐diabetic drug classes were tabulated. For disease progression, the coefficient of failure method was used for assessing data from two long‐term trials. The transit rate constants were remarkably consistent across 12 trials and represent system‐specific/drug‐independent parameters. The competitive landscape analysis showed that the primary efficacy metric fell into two groups of ΔHbA1c: >0.8% or ∼0.8%. On the safety endpoints, older agents showed some tolerability issues while the new agents were relatively safe. Among the different drug classes, only the thiazolidinediones appeared to slow disease progression but may also increase heart failure risk. In conclusion, the ability of system‐specific parameters to predict HbA1c provides a tool to predict the expected efficacy profile from abbreviated dose‐finding trials. To be commercially viable, new drugs should improve ΔHbA1c by about 0.8% or more and possess safety profiles similar to newer anti‐diabetic agents. Thus, this study proposes a suite of simple yet powerful tools to guide type‐2‐diabetes drug development. Copyright

Management of antipsychotic treatment discontinuation and interruptions using model-based simulations.

Background Medication nonadherence is a well described and prevalent clinical occurrence in schizophrenia. These pharmacokinetic model-based simulations analyze predicted antipsychotic plasma concentrations in nonadherence and treatment interruption scenarios and with treatment reinitiation. Methods Starting from steady state, pharmacokinetic model-based simulations of active moiety plasma concentrations of oral, immediate-release risperidone 3 mg/day, risperidone long-acting injection 37.5 mg/14 days, oral paliperidone extended-release 6 mg/day, and paliperidone palmitate 117 mg (75 mg equivalents)/28 days were assessed under three treatment discontinuation/interruption scenarios, ie, complete discontinuation, one week of interruption, and four weeks of interruption. In the treatment interruption scenarios, pharmacokinetic simulations were performed using medication-specific reinitiation strategies. Results Following complete treatment discontinuation, plasma concentrations persisted longest with paliperidone palmitate, followed by risperidone long-acting injection, while oral formulations exhibited the most rapid decrease. One week of oral paliperidone or risperidone interruption resulted in near complete elimination from the systemic circulation within that timeframe, reflecting the rapid elimination rate of the active moiety. After 1 and 4 weeks of interruption, minimum plasma concentrations were higher with paliperidone palmitate than risperidone long-acting injection over the simulated period. Four weeks of treatment interruption followed by reinitiation resulted in plasma levels returning to predicted therapeutic levels within 1 week. Conclusion Due to the long half-life of paliperidone palmitate (25–49 days), putative therapeutic plasma concentrations persisted longest in simulated cases of complete discontinuation or treatment interruption. These simulations may help clinicians better conceptualize the impact of antipsychotic nonadherence on plasma concentrations, and the impact of medication-specific reinitiation strategies after intermittent nonadherence.

Disease progression model for Clinical Dementia Rating–Sum of Boxes in mild cognitive impairment and Alzheimer’s subjects from the Alzheimer’s Disease Neuroimaging Initiative

Background The objective of this analysis was to develop a nonlinear disease progression model, using an expanded set of covariates that captures the longitudinal Clinical Dementia Rating Scale–Sum of Boxes (CDR–SB) scores. These were derived from the Alzheimer’s Disease Neuroimaging Initiative ADNI-1 study, of 301 Alzheimer’s disease and mild cognitive impairment patients who were followed for 2–3 years. Methods The model describes progression rate and baseline disease score as a function of covariates. The covariates that were tested fell into five groups: a) hippocampal volume; b) serum and cerebrospinal fluid (CSF) biomarkers; c) demographics and apolipoprotein Epsilon 4 (ApoE4) allele status; d) baseline cognitive tests; and e) disease state and comedications. Results Covariates associated with baseline disease severity were disease state, hippocampal volume, and comedication use. Disease progression rate was influenced by baseline CSF biomarkers, Trail-Making Test part A score, delayed logical memory test score, and current level of impairment as measured by CDR–SB. The rate of disease progression was dependent on disease severity, with intermediate scores around the inflection point score of 10 exhibiting high disease progression rate. The CDR–SB disease progression rate in a typical patient, with late mild cognitive impairment and mild Alzheimer’s disease, was estimated to be approximately 0.5 and 1.4 points/year, respectively. Conclusions In conclusion, this model describes disease progression in terms of CDR–SB changes in patients and its dependency on novel covariates. The CSF biomarkers included in the model discriminate mild cognitive impairment subjects as progressors and nonprogressors. Therefore, the model may be utilized for optimizing study designs, through patient population enrichment and clinical trial simulations.

Doripenem Dosing Recommendations for Critically Ill Patients Receiving Continuous Renal Replacement Therapy

Doripenem dosing regimens for patients receiving continuous venovenous hemofiltration (CVVH) and continuous venovenous hemodiafiltration (CVVHDF) were devised based on an established efficacy criterion (free plasma doripenem concentrations above the minimum inhibitory concentration [fT > MIC] of 1 mg/L for ≥35% of the dosing interval) while maintaining exposure below that with the highest studied dose of 1000 mg infused over 1 hour every 8 hours in healthy subjects. Simulations were utilized to assure ≥90% probability of achieving the efficacy criterion with the recommended doripenem regimens. Inflated intersubject variability of 40% (coefficient of variation) was used for pharmacokinetic parameters (representative of clinical variation) and nonrenal clearance was doubled to account for potential changes with acute renal insufficiency. Results indicate that a reduction in doripenem dose will be needed for critically ill patients receiving CVVH or CVVHDF. This work was conducted to fulfill a health authority request and resulted in the addition of dosing recommendations to the Doribax Summary of Product Characteristics.

Expansion of guidance for the day 8 initiation dose of paliperidone palmitate to avoid a missed dose

Video abstract Video

Optimizing Regions-of-Interest Composites for Capturing Treatment Effects on Brain Amyloid in Clinical Trials

BACKGROUND Pittsburgh Compound B (PiB) positron emission tomography (PET) neuroimaging is a powerful research tool to characterize amyloid evolution in the brain. Quantification of amyloid load critically depends on (i) the choice of a reference region (RR) and (ii) on the selection of regions of interest (ROIs) to derive the standard uptake value ratios (SUVRs). OBJECTIVE To evaluate the stability, i.e., negligible amyloid accumulation over time, of different RRs, and the performance of different PiB summary measures defined by selected ROIs and RRs for their sensitivity to detecting longitudinal change in amyloid burden. METHODS To evaluate RRs, cross-sectional and longitudinal analyses of focal regional and composite measures of amyloid accumulation were carried out on the standardized PiB-PET regional data for cerebellar grey matter (CER), subcortical white matter (SWM), and pons (PON). RRs and candidate composite SUVR measures were further evaluated to select regions and develop novel composites, using standardized 2-year change from baseline. RESULTS Longitudinal trajectories of PiB4-average of anterior cingulate (ACG), frontal cortex (FRC), parietal cortex, and precuneus-demonstrated marked variability and small change from baseline when normalized to CER, larger changes and less variability when normalized to SWM, which was further enhanced for the composite in PON-normalized settings. Novel composite PiB3, comprised of the average SUVRs of lateral temporal cortex, ACG, and FRC was created. CONCLUSION PON and SWM appeared to be more stable RRs than the CER. PiB3 showed compelling sample size reduction and gains in power calculations for clinical trials over conventional PiB4 composite.

A Novel Subject Synchronization Clinical Trial Design for Alzheimer's Disease

One of the challenges in developing a viable therapy for Alzheimers disease has been demonstrating efficacy within a clinical trial. Using this as motivation, we sought to re-examine conventional clinical trial practices in order to determine whether efficacy can be better shown through alternative trial designs and novel analysis methods. In this work, we hypothesize that the confounding factors which hamper the ability to discern a treatment signal are the variability in observations as well as the insidious nature of the disease. We demonstrate that a two-phase trial design in which drug dosing is administered after a certain level of disease severity has been reached, coupled with a method to account more accurately for the progression of the disease, may allow us to compensate for these factors, and thus enable us to make treatment effects more apparent. Utilizing data from two previously failed trials which involved the evaluation of galantamine for indication in mild cognitive impairment, we were able to demonstrate that a clear treatment effect can be realized through both visual and statistical means, and propose that future trials may be more likely to show success if similar methods are utilized.