Simulations Plus Research Articles

Integrating ANSYS Fluent Simulation and Aspen Plus for efficient heavy metal ion removal with de-oiled cake

In this study bio-flocculant extracted from the de-oiled cake, a common waste generated while producing oil was used to treat an electroplating industrial effluent. A comprehensive analysis of flocculation efficiency in a simulated reactor using ANSYS Workbench - Fluent, with a focus on optimizing impeller position was carried out. The findings reveal that the optimal impeller position is 2 cm from the tank base, where maximum turbulent kinetic energy (0.02577 m2/s2) and dissipation rate (22.19 m2/s3) were achieved at 200 RPM, enhancing flocculation efficacy. Furthermore, the study identifies 0.5 g L−1 as the optimal flocculant dosage, resulting in 72.70 % and 89.38 % removal of Ni(II) and Cr(VI), respectively. The flocculation process was determined to follow second-order kinetics, with significant improvements in collision efficiency and particle aggregation at the optimized dosage. Fourier Transform Infrared spectroscopy analysis confirmed chemical interactions leading to flocculation, while zeta potential and particle size distribution studies corroborated the stability and effectiveness of the process. A techno-economic analysis was conducted, estimating the total capital investment required for pilot and industrial-scale implementations, highlighting the feasibility and scalability of the proposed treatment method. A holistic framework for enhancing industrial effluent treatment processes by integrated computational fluid dynamics, kinetic studies, and economic evaluation, is the novelty of this research.

Best of both worlds: An expansion of the state of the art pKa model with data from three industrial partners.

In a unique collaboration between Simulations Plus and several industrial partners, we were able to develop a new version 11.0 of the previously published in silico pKa model, S+pKa, with considerably improved prediction accuracy. The model's training set was vastly expanded by large amounts of experimental data obtained from F. Hoffmann-La Roche AG, Genentech Inc., and the Crop Science division of Bayer AG. The previous v7.0 of S+pKa was trained on data from public sources and the Pharmaceutical division of Bayer AG. The model has shown dramatic improvements in predictive accuracy when externally validated on three new contributor compound sets. Less expected was v11.0's improvement in prediction on new compounds developed at Bayer Pharma after v7.0 was released (2013-2023), even without contributing additional data to v11.0. We illustrate chemical space coverage by chemistries encountered in the five domains, public and industrial, outline model construction, and discuss factors contributing to model's success.

Modeling, simulation and techno-economic evaluation of a micro-grid system based on steam gasification of organic municipal solid waste

The annual increase in population has led to rapid industrialization, urbanization, and economic growth over the years, becoming the lead cause of increased municipal solid waste (MSW) generation worldwide. In this study, MSW of 50 kg sampled by quartering method from Dandora dumpsite in Nairobi, Kenya is physically characterized and a sample of organic waste taken through proximate and ultimate analyses. Data obtained is used as input to a gasification model developed using advanced simulation and process engineering (Aspen) Plus software to provide characteristics of the yielded syngas. The obtained results are used to design a real gasification system and evaluate its techno-economic performance using HOMER® Pro as the tool for analysis. The techno-economic feasibility of the designed 60 kW gasification system is assessed considering a 165.59 kWh/day community-based load demand, with 11.5 % self-electricity consumption and 88.5 % grid feed. Results show organic and inorganic waste contents of 64.2 % and 35.8 %, respectively. The proximate and ultimate analyses reveal a high volatile matter content of 76.10 %, as well as fixed carbon content of 17.03 % in the biomass- rich-organic MSW. The simulated results show good agreement with experimental data and other simulation findings in literature. The study’s simulation output reveals 61.15 % H2, 1.42 % CH4, 24.12 % CO, 13.30 % CO2 contents following steam gasification at 700 °C. At 100 kg/h waste charge rate, a syngas yield of 2.39 m3/kg, gasification ratio of 2.2705 kg/kg and a lower heating value of 10,150 kJ/Nm3 (24.26 MJ/kg) are obtained. The sensitivity analysis results show increasing trend in CO2 and H2 syngas constituents from 0.4 to 1.0 steam-to-biomass ratio increase at 900 °C constant temperature: CO decrease is observed while CH4 values remain negligible and unaffected. The techno-economic findings reveal that the grid sales serve to offset the system’s annual costs by 39.34 %, obtaining a net present value (NPV) of $1.43 M, levelized cost of electricity (LCoE) of 0.155 $/kWh and 93.5 kg/yr. CO2 carbon footprint. An Organic MSW steam gasification micro grid is thus financially feasible in grid connected mode and is 99 % more carbon neutral compared to the Kenyan grid.

Advancing Fault Detection in HVAC Systems: Unifying Gramian Angular Field and 2D Deep Convolutional Neural Networks for Enhanced Performance.

Efficiency and comfort in buildings rely on on well-functioning HVAC systems. However, system faults can compromise performance. Modern data-driven fault detection methods, considering diverse techniques, encounter challenges in understanding intricate interactions and adapting to dynamic conditions present in HVAC systems during occupancy periods. Implementing fault detection during active operation, which aligns with real-world scenarios and captures dynamic interactions and environmental changes, is considered highly valuable. To address this, utilizing the dynamic simulation system HVAC SIMulation PLUS (HVACSIM+), an HVAC fault model was developed using 194 sensor signals from each HVAC component within a single-story, four-room building. The advanced HVAC fault detection framework, leveraging simulated HVAC operational scenarios with the Gramian angular field (GAF) and two-dimensional convolutional neural networks (GAF-2DCNNs), offers a robust and proactive solution. By utilizing the GAF capacity to convert time-series sensor data into informative 2D images, integrated with 2DCNN for automated feature extraction, hidden temporal relationships within 1D signals are captured. After training on nine significant HVAC faults and normal conditions during occupancy, the effectiveness of the proposed GAF-2DCNN is evaluated through comparisons with support vector machine (SVM), random forest (RF), and hybrid RF-SVM, one-dimensional convolutional neural networks (1D-CNNs). The results demonstrates an impressive overall accuracy of 97%, accompanied by precision, recall, and F1 scores that surpass 90% for individual HVAC faults. Through the introduction of the unified approach that integrates HVACSIM+ simulated data and GAF-2DCNN, a notable enhancement in robustness and reliability for handling substantial HVAC faults is achieved.

A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs

In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.

Conjunction of semi-mechanistic in vitro-in vivo modeling and population pharmacokinetics as a tool for virtual bioequivalence analysis - a case study for a BCS class II drug

Virtual bioequivalence trial (VBE) simulations based on (semi)mechanistic in vitro-in vivo (IVIV) modeling have gained a huge interest in the pharmaceutical industry. Sophisticated commercially available software allows modeling variable drug fates in the gastrointestinal tract (GIT). Surprisingly, the between-subject and inter-occasion variability (IOV) of the distribution volumes and clearances are ignored or simplified, despite substantially contributing to varied plasma drug concentrations. The paper describes a novel approach for IVIV-based VBE by using population pharmacokinetics (popPK). The data from two bioequivalence trials with a poorly soluble BCS class II drug were analyzed retrospectively. In the first trial, the test drug product (biobatch 1) did not meet the bioequivalence criteria, but after a reformulation, the second trial succeeded (biobatch 2). The popPK model was developed in the Monolix software (Lixoft SAS, Simulation Plus) based on the originator’s plasma concentrations. The modified Noyes-Whitney model was fitted to the results of discriminative biorelevant dissolution tests of the two biobatches and seven other reformulations. Then, the IVIV model was constructed by joining the popPK model with fixed drug disposition parameters, the drug dissolution model, and mechanistic approximation of the GIT transit. It was used to simulate the drug concentrations at different IOV levels of the primary pharmacokinetic parameters and perform the VBE. Estimated VBE success rates for both biobatches well reflected the outcomes of the bioequivalence trials. The predicted 90% confidence intervals for the area under the time-concentration curves were comparable with the observed values, and the 10% IOV allowed the closest approximation to the clinical results. Simulations confirmed that a significantly lower maximum drug concentration for biobatch 1 was responsible for the first clinical trial’s failure. In conclusion, the proposed workflow might aid formulation screening in generic drug development.

Determining the Relationship Between Clofazimine Gastrointestinal Levels and anti‐CryptosporidiumEfficacy in Mice

IntroductionCryptosporidiosis, the disease caused by parasitic infections with Cryptosporidium spp. can cause long‐term adverse impacts and even death in malnourished children and immunocompromised patients. The only FDA‐approved drug for treating cryptosporidiosis, nitazoxanide, has limited efficacy in the populations that are impacted most by the disease. A large‐scale phenotypic screening campaign identified clofazimine as active in both in vitro and in vivo preclinical models of Cryptosporidium infection. However, clofazimine was not effective in controlling Cryptosporidium infection in a subsequent Phase 2a clinical trial in HIV‐positive adults with cryptosporidiosis. Our recent pharmacokinetic/pharmacodynamic (PK/PD) modeling work demonstrated that plasma clofazimine levels observed in the Phase 2a clinical trial fell well below projected efficacious plasma concentrations estimated with preclinical in vivo data. Therefore, suboptimal clofazimine exposure in the Phase 2a trial likely contributed to the observed lack of efficacy. Nevertheless, plasma clofazimine levels may not represent local concentrations at the gastrointestinal (GI) tract, the site of action for treatment of Cryptosporidium infection. Thus, the objective of our current study is to build a physiologically based pharmacokinetic (PBPK) model of clofazimine in mice to simulate clofazimine GI pharmacokinetics (PK), estimate efficacious clofazimine concentrations at the tissue level, and explore important factors that can impact clofazimine exposure at the site of infection.MethodsC57BL/6 mice were administered a single oral dose of clofazimine at dose levels ranging 0.03‐300 mg/kg. Blood samples were collected up to 240 hours post‐dosing and GI samples were collected at 240 hours post‐dosing. In tandem, an efficacy study treated C. parvum‐infected IFN‐γ‐/‐ mice with equivalent clofazimine dosing and oocysts in fecal samples were quantified over time. GastroPlus (Simulations Plus) was used for modeling and simulation.ResultsThe final PBPK model captured clofazimine plasma PK in mice well across most doses (within a 2‐fold margin). The simulated clofazimine GI concentrations increased with increasing dose in a non‐proportional manner, similar to what was observed with the plasma PK. As expected for a drug with poor solubility, the non‐linearity was attributed to diminished drug dissolution at higher dose levels. The simulated clofazimine tissue concentrations were used to construct PK/PD models to predict efficacious tissue concentrations.ConclusionA clofazimine PBPK model was generated in mice to simulate clofazimine levels in plasma and the GI tract. By incorporating clofazimine efficacy data from a mouse model of Cryptosporidium infection, we identified a relationship between tissue drug concentrations and reduction in infection burden. Establishing a PK/PD relationship informed by GI drug levels will assist future drug development efforts for cryptosporidiosis.

Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work.

A dataset consisting of measured values for LogD, solubility, metabolic stability in human liver microsomes (HLMs), and Caco-2 permeability was used to evaluate the prediction models for lipophilicity (S+LogD), water solubility (S+Sw_pH), metabolic stability in HLM (CYP_HLM_Clint), intestinal permeability (S+Peff), and P-glycoprotein (P-gp) substrate identification (P-gp substrate) in the software ADMET Predictor (AP) from Simulations Plus. The dataset consisted of a total of 4,794 compounds, with at least data from metabolic stability determinations in HLM, from multiple discovery projects at Medivir. Our evaluation shows that the global AP models can be used for categorization of high and low values based on predicted results for metabolic stability in HLM and intestinal permeability, and to give good predictions of LogD (R2= 0.79), guiding the synthesis of new compounds and for prioritizing in vitro ADME experiments. The model seems to overpredict solubility for the Medivir compounds, however. We also used the in-house datasets to build local models for LogD, solubility, metabolic stability, and permeability by using artificial neural network (ANN) models in the optional Modeler module of AP. Predictions of the test sets were performed with both the global and the local models, and the R2 values for linear regression for predicted versus measured HLM in vitro intrinsic clearance (CLint) based on logarithmic data were 0.72 for the in-house model and 0.53 for the AP model. The improved predictions with the local models are likely explained both by the specific chemical space of the Medivir dataset and laboratory-specific assay conditions for parameters that require biologic assay systems. SIGNIFICANCE STATEMENT: AP is useful early in projects for predicting and categorizing LogD, metabolic stability, and permeability, to guide the synthesis of new compounds, and for prioritizing in vitro ADME experiments. The building of local in-house prediction models with the optional AP Modeler Module can yield improved prediction success since these models are built on data from the same experimental setup and can also be based on compounds with similar structures.

Multitask machine learning models for predicting lipophilicity (logP) in the SAMPL7 challenge

Accurate prediction of lipophilicity—logP—based on molecular structures is a well-established field. Predictions of logP are often used to drive forward drug discovery projects. Driven by the SAMPL7 challenge, in this manuscript we describe the steps that were taken to construct a novel machine learning model that can predict and generalize well. This model is based on the recently described Directed-Message Passing Neural Networks (D-MPNNs). Further enhancements included: both the inclusion of additional datasets from ChEMBL (RMSE improvement of 0.03), and the addition of helper tasks (RMSE improvement of 0.04). To the best of our knowledge, the concept of adding predictions from other models (Simulations Plus logP and logD@pH7.4, respectively) as helper tasks is novel and could be applied in a broader context. The final model that we constructed and used to participate in the challenge ranked 2/17 ranked submissions with an RMSE of 0.66, and an MAE of 0.48 (submission: Chemprop). On other datasets the model also works well, especially retrospectively applied to the SAMPL6 challenge where it would have ranked number one out of all submissions (RMSE of 0.35). Despite the fact that our model works well, we conclude with suggestions that are expected to improve the model even further.

Simulation of Remdesivir Pharmacokinetics and Its Drug Interactions.

Remdesivir, a drug originally developed against Ebola virus, is currently recommended for patients hospitalized with coronavirus disease of 2019 (COVID-19). In spite of United States Food and Drug Administration's recent assent of remdesivir as the only approved agent for COVID-19, there is limited information available about the physicochemical, metabolism, transport, pharmacokinetic (PK), and drug-drug interaction (DDI) properties of this drug. The objective of this in silico simulation work was to simulate the biopharmaceutical and DDI behavior of remdesivir and characterize remdesivir PK properties in special populations which are highly affected by COVID-19. The Spatial Data File format structures of remdesivir prodrug (GS-5734) and nucleoside core (GS-441524) were obtained from the PubChem database to upload into the GastroPlus software 9.8 version (Simulations Plus Inc., USA). The Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) Predictor and PKPlus modules of GastroPlus were used to simulate physicochemical and PK properties, respectively, in healthy and predisposed patients. Physiologically based pharmacokinetic (PBPK) modeling of GastroPlus was used to simulate different patient populations based on age, weight, liver function, and renal function status. Subsequently, these data were used in the Drug-Drug Interaction module to simulate drug interaction potential of remdesivir with other COVID-19 drug regimens and with agents used for comorbidities. Remdesivir nucleoside core (GS-441524) is more hydrophilic than the inactive prodrug (GS-5734) with nucleoside core demonstrating better water solubility. GS-5734, but not GS-441524, is predicted to be metabolized by CYP3A4. Remdesivir is bioavailable and its clearance is achieved through hepatic and renal routes. Differential effects of renal function, liver function, weight, or age were observed on the PK profile of remdesivir. DDI simulation study of remdesivir with perpetrator drugs for comorbidities indicate that carbamazepine, phenytoin, amiodarone, voriconazole, diltiazem, and verapamil have the potential for strong interactions with victim remdesivir, whereas agents used for COVID-19 treatment such as chloroquine and ritonavir can cause weak and strong interactions, respectively, with remdesivir. GS-5734 (inactive prodrug) appears to be a superior remdesivir derivative due to its hepatic stability, optimum hydrophilic/lipophilic balance, and disposition properties. Remdesivir disposition can potentially be affected by different physiological and pathological conditions, and by drug interactions from COVID-19 drug regimens and agents used for comorbidities.

Prediction of plasma concentrations using in silico modelling and simulation approach: Case of Acebutolol

The aim of this study was to predict the plasma concentrations of acebutolol tablets with different dissolution profiles using computer modelling and evaluating whether they are bioequivalent using simulated population studies. The dissolution behaviour of acebutolol was studied in the USP Apparatus-II using different dissolution media for pH 1.2, 4.5, and 6.8 at 37±0.5°C. The obtained dissolution data, as well as plasma concentration-time data of the reference product from the literature were used as inputs to build pharmacokinetic model of acebutolol within GastroPlus™ software (version 9.7, Simulations Plus Inc., Lancaster, CA, USA) to simulate the in vivo profiles of the drug. The dissolution profiles of the reference product Sectral® 400mg tablets and a locally produced generic product were>85% in 15min in three dissolution media. Simulation results demonstrated that the brand and generic products would show the same in vivo performance. Population simulation results of the ln-transformed 90% confidence interval for the ratio of Cmax, AUC0-t and AUC0-inf values for the two products were within the 80-125% interval, showing to be bioequivalent. Based on the in vitro results combined with in silico simulations using GastroPlus™, a biowaiver for immediate release acebutolol tablets is justified. Furthermore, computer modelling has shown to be a very intersting tool to prove the bioequivalence for these products.

Evaluation of quantitative structure property relationship algorithms for predicting plasma protein binding in humans

The extent of plasma protein binding is an important compound-specific property that influences a compound’s pharmacokinetic behavior and is a critical input parameter for predicting exposure in physiologically based pharmacokinetic (PBPK) modeling. When experimentally determined fraction unbound in plasma (fup) data are not available, quantitative structure–property relationship (QSPR) models can be used for prediction. Because available QSPR models were developed based on training sets containing pharmaceutical-like compounds, we compared their prediction accuracy for environmentally relevant and pharmaceutical compounds. Fup values were calculated using Ingle et al., Watanabe et al. and ADMET Predictor (Simulation Plus). The test set included 818 pharmaceutical and environmentally relevant compounds with fup values ranging from 0.01 to 1. Overall, the three QSPR models resulted in over-prediction of fup for highly binding compounds and under-prediction for low or moderately binding compounds. For highly binding compounds (0.01 ≤ fup ≤ 0.25), Watanabe et al. performed better with a lower mean absolute error (MAE) of 6.7% and a lower mean absolute relative prediction error (RPE) of 171.7% than other methods. For low to moderately binding compounds, both Ingle et al. and ADMET Predictor performed better than Watanabe et al. with superior MAE and RPE values. The positive polar surface area, the number of basic functional groups and lipophilicity were the most important chemical descriptors for predicting fup.This study demonstrated that the prediction of fup was the most uncertain for highly binding compounds. This suggested that QSPR-predicted fup values should be used with caution in PBPK modeling.

In Silico Prediction of Cytochrome P450‐Based Drug‐Drug Interactions in Breast Cancer Treatment Regimens

IntroductionBreast cancer is one of the most frequently diagnosed cancers in US. Its treatment regimens are complex as they involve premedications and concomitant medications, and can result in drug‐drug interactions (DDI). There are limited data available on cytochrome P450 (CYP)‐based drug interactions within the breast cancer regimens. The goal of the current study was to predict DDI between the drugs in the breast cancer treatment regimens.MethodsUsing the National Comprehensive Cancer Network (NCCN) guidelines the treatment regimens for breast cancer were identified. In silico DDI predictions were performed at steady state simulation using DDI module of the GastroPlus software provided by Simulation Plus Inc. The premedication and concomitantly administered drugs were evaluated as combinations with chemotherapeutic agents. Various enzyme inhibition kinetics parameters (e.g., Ki and EC50) required for simulation were obtained from the recent literature.ResultsUsing the simulated and calculated gut and liver unbound perpetrator concentration during the steady state simulation, combinations of drugs showed differential DDI potential. Aprepitant, an antiemetic agent, showed moderate to strong inhibition when used with docetaxel, paclitaxel, abemaciclib and olaparib. In certain cases calculated liver perpetrator concentration showed a stronger inhibitory prediction of chemotherapeutic agents compared to simulated liver perpetrator concentration. Results obtained with netupitant varied from weak to strong inhibitory interactions with chemotherapeutic agents.ConclusionsIn silico analyses appear to confirm that breast cancer treatment regimens may interact with each other and influence plasma concentrations of chemotherapeutic agents. We conclude that toxicity or therapeutic failure from anticancer agents could be due to interactions within the breast cancer treatment regimens.Support or Funding InformationThe GastroPlus software 9.5 version was provided by Simulations Plus, Inc. (Lancaster, CA)

Predicting pKa for Small Molecules on Public and In-house Datasets Using Fast Prediction Methods Combined with Data Fusion.

Data fusion approach was investigated in the context of pKa prediction for 391 small molecules derived from a public data source as well as for 681 compounds from an internal corporate database. Four different pKa prediction methods (Simulations Plus ADMET-Predictor S+pKa, ACD/Labs Percepta Classic, ACD/Labs Percepta GALAS and Epik) were used to predict the most acidic or basic pKa for each of the compounds. By using data fusion, the median absolute error for the internal compounds was reduced from the best performing single model's value of 0.69 down to 0.50. In addition to the improved accuracy, data fusion also enabled predictions for all of the compounds in the dataset as individual methods failed on some of the molecules.

In Silico Identification of Physicochemical and Pharmacokinetic Properties of Vitamin D3 and Its Derivatives

BackgroundVitamin D3 is an endogenous substance either biosynthesized in humans or absorbed from diet and health supplements. Although calcitriol, the most active form of vitamin D3, is primarily responsible for the health benefits of vitamin D3 including bone and anticancer functions, there are several derivatives in the metabolic pathways of vitamin D3. Calcitriol has largely been the focus of the disposition experimental studies but not the other vitamin D3 derivatives. The objective of this study was to identify the physicochemical and pharmacokinetic properties of vitamin D3 derivatives in silico.MethodsThirteen chemical structures of vitamin D3 derivatives were obtained from the PubChem and PubMed databases. The structures were imported to the GastroPlus software and simulations were run using the Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) Predictor module. The ADMET Predictor data were used to simulate the pharmacokinetic properties using physiologically‐based pharmacokinetic (PBPKPlus) modeling. The ADMET Predictor module provided the structure‐based physicochemical properties, cytochrome P450‐based metabolism and the ability to cross blood‐brain barrier by vitamin D3 derivatives. In comparison, the PBPKPlus module predicted the pharmacokinetic properties (e.g., bioavailability, half‐life, clearance) based on the physicochemical parameters. It also developed pharmacokinetic curves for the vitamin D3 derivatives and simulated concentration versus time plots. Microsoft Excel module was used to compile the data obtained and run lipophilicity (Log P) versus fraction absorbed (Fa) correlation analyses.ResultsThe predicted hydrogen ion (pH) values ranged from 3.0 to 9.0 with calcitriol having a lipophilicity of 5.5 units. The structures with more hydroxyl groups have better solubility values than the structures with fewer hydroxyl groups. The fraction absorbed values for the vitamin D3 derivatives were low except for calcitroic acid, 1,23,25‐trihydroxy‐24‐oxo‐vitamin D3, and 1,25‐dihydroxyvitamin D3‐26,23‐lactone each being greater than 90% fraction absorbed. The half‐lives ranged from 1.2 to 8.0 hours with 1,23,25‐trihydroxyvitamin D3 having the lowest half‐life of 1.2 hours. Most of the vitamin D3 derivatives have high blood‐brain barrier penetration ability except for calcitetrol, 1,23,25 trihydroxyvitamin D3, and 1,23,25‐trihydroxy‐24‐oxo‐vitamin D3. The lipophilicity (Log P) versus fraction absorbed (Fa) of all the vitamin D3 derivatives studied yielded a correlation (r2) of 0.66.ConclusionSimulation data from GastroPlus indicate that vitamin D3 pathway has several structurally‐related compounds with differential physicochemical and pharmacokinetic properties that ranged from being hydrophilic to lipophilic and thus influencing their plasma concentration. The hydrophilic derivatives of vitamin D3 have higher fraction absorbed over the lipophilic ones. Understanding the ADME properties of vitamin D3 derivatives with the knowledge of pharmacological potency could influence the identification of pharmacokinetically most acceptable vitamin D3 derivative for routine supplementation.Support or Funding InformationGastroPlus software was provided by Simulations Plus, Inc. (Lancaster, CA).This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

In Silico Prediction of the Absorption and Disposition of Cefadroxil in Humans using an Intestinal Permeability Method Scaled from Humanized PepT1 Mice.

It is difficult to predict the pharmacokinetics and plasma concentration-time profiles of new chemical entities in humans based on animal data. Some pharmacokinetic parameters, such as clearance and volume of distribution, can be scaled allometrically from rodents, mammals, and nonhuman primates with good success. However, it is far more challenging to predict the oral pharmacokinetics of experimental drug candidates. In the present study, we used in situ estimates of intestinal permeability, obtained in silico and from rat, wild-type (WT), and humanized PepT1 (huPepT1) mice, to predict the systemic exposure of cefadroxil, an orally administered model compound, under a variety of conditions. Using the GastroPlus simulation software program (Simulations Plus, Lancaster, CA), we found that the C max and area under the plasma concentration-time curve from time zero to the last measurable concentration of cefadroxil were better predicted using intestinal permeability estimates (both segmental and jejunal) from huPepT1 than from WT mice, and that intestinal permeabilities based on in silico and rat estimates gave worse predictions. We also observed that accurate predictions were possible for cefadroxil during oral dose escalation (i.e., 5, 15, and 30 mg/kg cefadroxil), a drug-drug interaction study (i.e., 5 mg/kg oral cefadroxil plus 45 mg/kg oral cephalexin), and an oral multiple dose study [i.e., 500 mg (6.7 mg/kg) cefadroxil every 6 hours]. Finally, the greatest amount of cefadroxil was absorbed in duodenal and jejunal segments of the small intestine after a 5 mg/kg oral dose. Thus, by combining a humanized mouse model and in silico software, the present study offers a novel strategy for better translating preclinical pharmacokinetic data to oral drug exposure during first-in-human studies.

Metabolism of Carfentanil, an Ultra-Potent Opioid, in Human Liver Microsomes and Human Hepatocytes by High-Resolution Mass Spectrometry.

Carfentanil is an ultra-potent synthetic opioid. No human carfentanil metabolism data are available. Reportedly, Russian police forces used carfentanil and remifentanil to resolve a hostage situation in Moscow in 2002. This alleged use prompted interest in the pharmacology and toxicology of carfentanil in humans. Our study was conducted to identify human carfentanil metabolites and to assess carfentanil's metabolic clearance, which could contribute to its acute toxicity in humans. We used Simulations Plus's ADMET Predictor™ and Molecular Discovery's MetaSite™ to predict possible metabolite formation. Both programs gave similar results that were generally good but did not capture all metabolites seen in vitro. We incubated carfentanil with human hepatocytes for up to 1h and analyzed samples on a Sciex 3200 QTRAP mass spectrometer to measure parent compound depletion and extrapolated that to represent intrinsic clearance. Pooled primary human hepatocytes were then incubated with carfentanil up to 6 h and analyzed for metabolite identification on a Sciex 5600+ TripleTOF (QTOF) high-resolution mass spectrometer. MS and MS/MS analyses elucidated the structures of the most abundant metabolites. Twelve metabolites were identified in total. N-Dealkylation and monohydroxylation of the piperidine ring were the dominant metabolic pathways. Two N-oxide metabolites and one glucuronide metabolite were observed. Surprisingly, ester hydrolysis was not a major metabolic pathway for carfentanil. While the human liver microsomal system demonstrated rapid clearance by CYP enzymes, the hepatocyte incubations showed much slower clearance, possibly providing some insight into the long duration of carfentanil's effects.

Abstract 382: Concomitant administration of acid reducing agents can impact the pharmacokinetics of the balanced pan-PI3K and mTOR inhibitor PQR309

Abstract Introduction Acid reducing agents (ARAs) - such as proton pump inhibitors, H2-antagonists or antacids - are commonly prescribed medications to reduce stomach acidity in cancer patients. Increased gastric pH can significantly impair the dissolution of agents that exhibit pH-dependent solubility, thus possibly reducing their absorption and influence their pharmacokinetics (PK). PQR309 is a novel oral balanced dual pan-PI3K and mTOR inhibitor currently in clinical development for treatment of solid tumors and hematological malignancies. PQR309 is a weak base and exhibits pH-dependent solubility. Therefore, the assessment of the potential for drug-drug interactions with ARAs is essential to avoid treatment failure in cancer patients. Methods GastroPlusTM v.9.0 (Simulations Plus, Inc.) was used to build a physiologically-based absorption and pharmacokinetic (PBPK) model of PQR309. A baseline model was built with the physicochemical properties of the compound and pre-clinical PK data after iv and po administration. The model was then extended using PK data from cancer patients. Volume of distribution and renal clearance were calculated using the Lukacova and fup*GFR methods, respectively. Hepatic clearance was estimated from in vitro clearance obtained in hepatocytes and the intestinal absorption was calculated by the Advanced Compartmental Absorption and Transit (ACATTM) Model. Results The parameter sensitivity analysis showed that at fasted stomach pH of ∼1.5, PQR309 is fully dissolved and rapidly absorbed in the upper part of gastrointestinal tract leading to a distinct maximum plasma concentration (Cmax) and a predicted bioavailability of almost 100%. At a stomach pH of 4.0 dissolution is delayed and the absorption shifted towards distal parts of the gastrointestinal tract. The predicted bioavailability is still estimated to be good (> 75%), but the observed plasma concentration time profile is much shallower and the expected Cmax considerably reduced. Conclusions Our model predicts that concomitant administration of ARAs can impact PQR309 absorption and change its pharmacokinetic profile. The clinical relevance of this potential drug-drug interaction is unknown. In currently ongoing clinical studies co-medication of PQR309 with ARAs should be avoided until further clinical investigations confirm the model predictions and mitigation strategies are explored. Citation Format: Karin Jorga, Eva Huehn, Grace Fraczkiewicz, Debora Schmitz, Doriano Fabbro, Sasa Dimitrijevic. Concomitant administration of acid reducing agents can impact the pharmacokinetics of the balanced pan-PI3K and mTOR inhibitor PQR309. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 382.

Modeling ADMET.

Drug discovery and development is a costly and time-consuming endeavor (Calcoen et al. Nat Rev Drug Discov 14(3):161-162, 2015; The truly staggering cost of inventing new drugs. Forbes. http://www.forbes.com/sites/matthewherper/2012/02/10/the-truly-staggering-cost-of-inventing-new-drugs/, 2012; Scannell et al. Nat Rev Drug Discov 11(3):191-200, 2012). Over the last two decades, computational tools and in silico models to predict ADMET (Adsorption, Distribution, Metabolism, Excretion, and Toxicity) profiles of molecules have been incorporated into the drug discovery process mainly in an effort to avoid late-stage failures due to poor pharmacokinetics and toxicity. It is now widely recognized that ADMET issues should be addressed as early as possible in drug discovery. Here, we describe in detail how ADMET models can be developed and applied using a commercially available package, ADMET Predictor™ 7.2 (ADMET Predictor v7.2. Simulations Plus, Inc., Lancaster, CA, USA).

Abstract A135: Physiologically-based pharmacokinetic modeling to support clinical investigation of the EZH2 inhibitor, tazemetostat (EPZ-6438) in INI1-deficient pediatric tumors

Abstract Tazemetostat (EPZ-6438) is a selective small molecule inhibitor of the histone-lysine methyltransferase EZH2, which has been implicated in the pathogenesis of a variety of malignancies, including B-cell non-Hodgkin lymphoma and INI1-deficient tumors. INI1-negative malignant rhabdoid tumors, for instance, occur mainly in young children. Tazemetostat pharmacokinetic (PK) data from the first in human phase I clinical study (CT.gov: NCT01897571), across the dose range of 100 mg (suspension) and 100, 200, 400, 800 and 1600 mg (tablet) p.o. twice daily (BID), together with in vitro data including plasma protein binding, blood partitioning, metabolic stability and P450 phenotyping were used to simulate adult exposures by physiologically-based PK (PBPK) modeling (GastroPlus™ 8.5, Simulations Plus, Inc.). A model fit of the adult exposures (n = 24) that, based on visual inspection, adequately described the time-concentration profiles of tazemetostat resulted in prediction of mean steady-state AUC0-t and oral clearance (CL/F) within ±30% of the observed results across the dose range. In addition, mean steady-state Cmax were predicted ≤2-fold of the observed values, for both suspension and tablet formulations. The resultant model was then used to simulate tazemetostat steady-state exposures in discrete pediatric age ranges (6 month to 1 year (yr), >1-2 yrs, >2-6 yrs, >6-12 yrs, >12-18 yrs) following BID administration of an oral suspension. In addition to pediatric physiology, the simulations accounted for ontogeny in hematocrit, plasma protein levels and CYP expression. Using this exposure-based analysis, pediatric doses which afforded the target AUC (80% of adult steady-state AUC0-t at 800 mg or 390 mg/m2 BID) were identified. On a body surface area normalized basis, the projected doses were comparable across the age range (1 to 18 yrs), from 270 to 305 mg/m2 BID, with a slightly lower projected dose of 230 mg/m2BID, for the 6 month to 1 yr old group. As the projected doses by age were comparable, population simulations were performed to determine the corresponding exposures for each age range, at a fixed 300 mg/m2 BID dose. At this dose, mean steady-state AUC0-t values ranged from approximately 80% to 100% of that observed in adults at 800 mg BID. Mean steady-state Cmax was projected to range between 895 and 1550 ng/mL (110% to 190% of Cmax,ss at 800 mg BID in adult), but within the safe and efficacious exposure range defined in adult at doses up to 1600 mg BID. This study demonstrates the prospective application of PBPK early in clinical development to support clinical trial design in special populations, and has provided dosing recommendations for future tazemetostat trials in pediatric patients, ages 6 months to 18 years. Citation Format: Nathalie Rioux, Heike Keilhack, Blythe Thomson, Richard Chesworth, Robert A. Copeland, Eric Hedrick, Peter Ho, Nigel J. Waters. Physiologically-based pharmacokinetic modeling to support clinical investigation of the EZH2 inhibitor, tazemetostat (EPZ-6438) in INI1-deficient pediatric tumors. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A135.