A novel <i>in vitro</i> allometric scaling methodology for aldehyde oxidase substrates to enable selection of appropriate species for traditional allometry

<p>1. Failure to predict human pharmacokinetics of aldehyde oxidase (AO) substrates using traditional allometry has been attributed to species differences in AO metabolism.</p> <p>2. To identify appropriate species for predicting human <i>in vivo</i> clearance by single-species scaling (SSS) or multispecies allometry (MA), we scaled <i>in vitro</i> intrinsic clearance (CL_int) of five AO substrates obtained from hepatic S9 of mouse, rat, guinea pig, monkey and minipig to human <i>in vitro</i> CL_int.</p> <p>3. When predicting human <i>in vitro</i> CL_int, average absolute fold-error was ≤2.0 by SSS with monkey, minipig and guinea pig (rat/mouse >3.0) and was <3.0 by most MA species combinations (including rat/mouse combinations).</p> <p>4. Interspecies variables, including fraction metabolized by AO (F_m,AO) and hepatic extraction ratios (E) were estimated <i>in vitro</i>. SSS prediction fold-errors correlated with the animal:human ratio of E (<i>r</i>² = 0.6488), but not F_m,AO (<i>r</i>² = 0.0051).</p> <p>5. Using plasma clearance (CL_p) from the literature, SSS with monkey was superior to rat or mouse at predicting human CL_p of BIBX1382 and zoniporide, consistent with <i>in vitro</i> SSS assessments.</p> <p>6. Evaluation of <i>in vitro</i> allometry, F_m,AO and E may prove useful to guide selection of suitable species for traditional allometry and prediction of human pharmacokinetics of AO substrates.</p

Medication Exposure in Highly Adherent Psychiatry Patients

Medication exposure is dependent upon many factors, the single most important being if the patient took the prescribed medication as indicated. To assess medication exposure for psychotropic and other medication classes, we enrolled 115 highly adherent psychiatry patients prescribed five or more medications. In these patients, we measured 21 psychotropic and 38 nonpsychotropic medications comprising a 59 medication multiplex assay panel. Strict enrollment criteria and reconciliation of the electronic health record medication list prior to study initiation produced a patient cohort that was adherent with 91% of their prescribed medications as determined by comparing medications detected empirically in blood to the electronic health record medication list. In addition, 13% of detected medications were not in the electronic health record medication list. We found that only 53% of detected medications were within the literature-derived reference range with 41% below and 6% above the reference range specific to each medication. When psychotropic medications were analyzed near trough-level, only sertraline was found to be within the literature-derived reference range for all patients tested. Concentrations of the remaining medications indicated extensive exposure below the reference range. This is the first study to empirically and comprehensively assess medication exposure obtained in comorbid polypharmacy patients, minimizing the important behavioral factor of adherence in the study of medication exposure. These data indicate that low medication exposure is extensive and must be considered when therapeutic issues arise, including the lack of response to medication therapy

Medication adherence, medical record accuracy, and medication exposure in real-world patients using comprehensive medication monitoring

<div><p>Background</p><p>Poor adherence to medication regimens and medical record inconsistencies result in incomplete knowledge of medication therapy in polypharmacy patients. By quantitatively identifying medications in the blood of patients and reconciling detected medications with the medical record, we have defined the severity of this knowledge gap and created a path toward optimizing medication therapy.</p><p>Methods and findings</p><p>We validated a liquid chromatography-tandem mass spectrometry assay to detect and/or quantify 38 medications across a broad range of chronic diseases to obtain a comprehensive survey of patient adherence, medical record accuracy, and exposure variability in two patient populations. In a retrospectively tested 821-patient cohort representing U.S. adults, we found that 46% of medications assessed were detected in patients as prescribed in the medical record. Of the remaining medications, 23% were detected, but not listed in the medical record while 30% were prescribed to patients, but not detected in blood. To determine how often each detected medication fell within literature-derived reference ranges when taken as prescribed, we prospectively enrolled a cohort of 151 treatment-regimen adherent patients. In this cohort, we found that 53% of medications that were taken as prescribed, as determined using patient self-reporting, were not within the blood reference range. Of the medications not in range, 83% were below and 17% above the lower and upper range limits, respectively. Only 32% of out-of-range medications could be attributed to short oral half-lives, leaving extensive exposure variability to result from patient behavior, undefined drug interactions, genetics, and other characteristics that can affect medication exposure.</p><p>Conclusions</p><p>This is the first study to assess compliance, medical record accuracy, and exposure as determinants of real-world treatment and response. Variation in medication detection and exposure is greater than previously demonstrated, illustrating the scope of current therapy issues and opening avenues that warrant further investigation to optimize medication therapy.</p></div

Distribution of total detected medications for two cohorts.

<p>Percent of patients having between 0 and 8 detected medications in the Residuals vs. Reconciled cohorts.</p

Detection rate for panel medications in two cohorts.

<p>Percent of patients for whom a given medication is detected in Residuals vs. Reconciled Cohorts. The dotted line indicates equal detection rates in both cohorts, while the solid line indicates the ratio of overall detection rate in both cohorts: 1.3 detected drugs per patient in Residuals Cohort vs. 3.2 detected drugs per patient in Reconciled Cohort.</p

Discovery of (<i>R</i>)‑(2-Fluoro-4-((-4-methoxyphenyl)ethynyl)phenyl) (3-Hydroxypiperidin-1-yl)methanone (ML337), An mGlu₃ Selective and CNS Penetrant Negative Allosteric Modulator (NAM)

A multidimensional, iterative parallel synthesis effort identified a series of highly selective mGlu₃ NAMs with submicromolar potency and good CNS penetration. Of these, ML337 resulted (mGlu₃ IC₅₀ = 593 nM, mGlu₂ IC₅₀ >30 μM) with B:P ratios of 0.92 (mouse) to 0.3 (rat). DMPK profiling and shallow SAR led to the incorporation of deuterium atoms to address a metabolic soft spot, which subsequently lowered both in vitro and in vivo clearance by >50%

Medication panel characteristics and detection relative to prescription record for two patient cohorts.

<p>Medication panel characteristics and detection relative to prescription record for two patient cohorts.</p

Medication detections vs. therapeutic monitoring ranges in Reconciled Cohort.

<p>Percent of medications detected quantitatively below, within or above ranges established in the therapeutic drug monitoring literature, for drugs that were listed in the patients EHR. Error bars were calculating from Bernoulli trials.</p

Adherence and non-prescribed medication use in two cohorts.

<p>A) Percent of prescribed medications that are detected (adherence), for medications having 10 or more prescriptions in each cohort. B) Percent of detected medications not in the EHR (non-prescribed), for medications having 10 or more detections in each cohort. The solid diagonal line indicates equality in both cohorts, and the dashed line indicates the overall ratio of adherence or non-prescribed use between cohorts, calculated across all medications. Markers are sized proportionally to log10 of prescriptions or detections.</p

Percent of prescribed medications that are detected vs. medication half-life for Reconciled Cohort.

<p>Medications with half-life > 24 hours are shown at 24 hours on the abscissa. The fit denotes the least-squares power curve; the functional form was selected due to expected exponential decay of medication concentration with time.</p