Physiologically Based Pharmacokinetic Modeling in Lead Optimization. 2. Rational Bioavailability Design by Global Sensitivity Analysis To Identify Properties Affecting Bioavailability

Assessing Dose-Exposure-Response Relationships of Miltefosine in Adults and Children using Physiologically-Based Pharmacokinetic Modeling Approach

OBJECTIVES

Miltefosine is the first and only oral medication to be successfully utilized as an antileishmanial agent. However, the drug is associated with differences in exposure patterns and cure rates among different population groups e.g. ethnicity and age (i.e., children v adults) in clinical trials. In this work, mechanistic population physiologically-based pharmacokinetic (PBPK) models have been developed to study the dose-exposure-response relationship of miltefosine in in silico clinical trials and evaluate the differences in population groups, particularly children and adults.

METHODS

The Simcyp population pharmacokinetics platform was employed to predict miltefosine exposure in plasma and peripheral blood mononuclear cells (PBMCs) in a virtual population under different dosing regimens. The cure rate of a simulation was based on the percentage of number of the individual virtual subjects with AUCd0-28 > 535 µg⋅day/mL in the virtual population.

RESULTS

It is shown that both adult and paediatric PBPK models of miltefosine can be developed to predict the PK data of the clinical trials accurately. There was no significant difference in the predicted dose-exposure-response of the miltefosine treatment for different simulated ethnicities under the same dose regime and the dose-selection strategies determined the clinical outcome of the miltefosine treatment. A lower cure rate of the miltefosine treatment in paediatrics was predicted because a lower exposure of miltefosine was simulated in virtual paediatric in comparison with adult virtual populations when they received the same dose of the treatment.

CONCLUSIONS

The mechanistic PBPK model suggested that the higher fraction of unbound miltefosine in plasma was responsible for a higher probability of failure in paediatrics because of the difference in the distribution of plasma proteins between adults and paediatrics. The developed PBPK models could be used to determine an optimal miltefosine dose regime in future clinical trials.

Multi-dimensional structural footprint identification for the design of potential scaffolds targeting METTL3 in cancer treatment from natural compounds

CONTEXT

[Formula: see text]-adenosine-methyltransferase (METTL3) is the catalytic domain of the 'writer' proteins which is involved in the post modifications of [Formula: see text]-methyladinosine ([Formula: see text]). Though its activities are essential in many biological processes, it has been implicated in several types of cancer. Thus, drug developers and researchers are relentlessly in search of small molecule inhibitors that can ameliorate the oncogenic activities of METTL3. Currently, STM2457 is a potent, highly selective inhibitor of METTL3 but is yet to be approved.

METHODS

In this study, we employed structure-based virtual screening through consensus docking by using AutoDock Vina in PyRx interface and Glide virtual screening workflow of Schrodinger Glide. Thermodynamics via MM-PBSA calculations was further used to rank the compounds based on their total free binding energies. All atom molecular dynamics simulations were performed using AMBER 18 package. FF14SB force fields and Antechamber were used to parameterize the protein and compounds respectively. Post analysis of generated trajectories was analyzed with CPPTRAJ and PTRAJ modules incorporated in the AMBER package while Discovery studio and UCSF Chimera were used for visualization, and origin data tool used to plot all graphs.

RESULTS

Three compounds with total free binding energies higher than STM2457 were selected for extended molecular dynamics simulations. The compounds, SANCDB0370, SANCDB0867, and SANCDB1033, exhibited stability and deeper penetration into the hydrophobic core of the protein. They engaged in relatively stronger intermolecular interactions involving hydrogen bonds with resultant increase in stability, reduced flexibility, and decrease in the surface area of the protein available for solvent interactions suggesting an induced folding of the catalytic domain. Furthermore, in silico pharmacokinetics and physicochemical analysis of the compounds revealed good properties suggesting these compounds could serve as promising MEETL3 entry inhibitors upon modifications and optimizations as presented by natural compounds. Further biochemical testing and experimentations would aid in the discovery of effective inhibitors against the berserk activities of METTL3.

Structure-based identification of novel scaffolds as potential HIV-1 entry inhibitors involving CCR5

C-C chemokine receptor 5 (CCR5), which is part of the chemokine receptor family, is a member of the G protein-coupled receptor superfamily. The interactions of CCR5 with HIV-1 during viral entry position it as an effective therapeutic target for designing potent antiviral therapies. The small-molecule Maraviroc was approved by the FDA as a CCR5 drug in 2007, while clinical trials failure has characterised many of the other CCR5 inhibitors. Thus, the continual identification of potential CCR5 inhibitors is, therefore, warranted. In this study, a structure-based discovery approach has been utilised to screen and retrieved novel potential CCR5 inhibitors from the Asinex antiviral compound (∼ 8,722) database. Explicit lipid-bilayer molecular dynamics simulation, in silico physicochemical and pharmacokinetic analyses, were further performed for the top compounds. A total of 23 structurally diverse compounds with binding scores higher than Maraviroc were selected. Subsequent molecular dynamics (MD) simulations analysis of the top four compounds LAS 51495192, BDB 26405401, BDB 26419079, and LAS 34154543, maintained stability at the CCR5 binding site. Furthermore, these compounds made pertinent interactions with CCR5 residues critical for the HIV-1 gp120-V3 loop binding such as Trp86, Tyr89, Phe109, Tyr108, Glu283 and Tyr251. Additionally, the predicted in silico physicochemical and pharmacokinetic descriptors of the selected compounds were within the acceptable range for drug-likeness. The results suggest positive indications that the identified molecules may represent promising CCR5 entry inhibitors. Further structural optimisations and biochemical testing of the proposed compounds may assist in the discovery of effective HIV-1 therapy.Communicated by Ramaswamy H. Sarma.

Characterization of the binding of MRTX1133 as an avenue for the discovery of potential KRASG12D inhibitors for cancer therapy

The Kirsten rat sarcoma (KRAS) oncoprotein has been on drug hunters list for decades now. Initially considered undruggable, recent advances have successfully broken the jinx through covalent inhibition that exploits the mutated cys12 in the switch II binding pocket (KRAS^G12C). Though this approach has achieved some level of success, patients with mutations other than cys12 are still uncatered for. KRAS^G12D is the most frequent KRAS mutated oncoprotein. It is only until recently, MRTX1133 has been discovered as a potential inhibitor of KRAS^G12D. This study seeks to unravel the structural binding mechanism of MRTX1133 as well as identify potential drug leads of KRAS^G12D based on structural binding characteristics of MRTX1133. It was revealed that MRTX1133 binding stabilizes the binding site by increasing the hydrophobicity which resultantly induced positive correlated movements of switches I and II which could disrupt their interaction with effector and regulatory proteins. Furthermore, MRTX1133 interacted with critical residues; Asp69 (- 4.54 kcal/mol), His95 (- 3.65 kcal/mol), Met72 (- 2.27 kcal/mol), Thr58 (- 2.23 kcal/mol), Gln99 (- 2.03 kcal/mol), Arg68 (- 1.67 kcal/mol), Tyr96 (- 1.59 kcal/mol), Tyr64 (- 1.34 kcal/mol), Gly60 (- 1.25 kcal/mol), Asp12 (- 1.04 kcal/mol), and Val9 (- 1.03 kcal/mol) that contributed significantly to the total free binding energy of - 73.23 kcal/mol. Pharmacophore-based virtual screening based on the structural binding mechanisms of MRTX1133 identified ZINC78453217, ZINC70875226 and ZINC64890902 as potential KRAS^G12D inhibitors. Further, structural optimisations and biochemical testing of these compounds would assist in the discovery of effective KRAS^G12D inhibitors.

Streamlining physiologically‐based pharmacokinetic model design for intravenous delivery of nanoparticle drugs

Physiologically‐based pharmacokinetic (PBPK) modeling for nanoparticles elucidates the nanoparticle drug’s disposition in the body and serves a vital role in drug development and clinical studies. This paper offers a systematic and tutorial‐like approach to developing a model structure and writing distribution ordinary differential equations based on asking binary questions involving the physicochemical nature of the drug in question. Further, by synthesizing existing knowledge, we summarize pertinent aspects in PBPK modeling and create a guide for building model structure and distribution equations, optimizing nanoparticle and non‐nanoparticle specific parameters, and performing sensitivity analysis and model validation. The purpose of this paper is to facilitate a streamlined model development process for students and practitioners in the field.

Natural phyto, compounds as possible noncovalent inhibitors against SARS-CoV2 protease: computational approach

At present, there is no cure or vaccine for the devastating new highly infectious severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has affected people globally. Herein, we identified potent phytocompounds from two antiviral plants Momordica charantia L. and Azadirachta indica used locally for the treatment of viral and parasitic infections. Structure-based virtual screening and molecular dynamics (MD) simulation have been employed to study their inhibitory potential against the main protease (Mpro) SARS-CoV-2. A total of 86 compounds from M. charantia L. and A. indica were identified. The top six phytocompounds; momordicine, deacetylnimninene, margolonone, momordiciode F2, nimbandiol, 17-hydroxyazadiradione were examined and when compared with three FDA reference drugs (remdesivir, hydroxychloroquine and ribavirin). The top six ranked compounds and FDA drugs were then subjected to MD simulation and pharmacokinetic studies. These phytocompounds showed strong and stable interactions with the active site amino acid residues of SARS-CoV-2 Mpro similar to the reference compound. Results obtained from this study showed that momordicine and momordiciode F2 exhibited good inhibition potential (best MMGBA-binding energies; -41.1 and -43.4 kcal/mol) against the Mpro of SARS-CoV-2 when compared with FDA reference anti-viral drugs (Ribavirin, remdesivir and hydroxychloroquine). Per-residue analysis, root mean square deviation and solvent-accessible surface area revealed that compounds interacted with key amino acid residues at the active site of the enzyme and showed good system stability. The results obtained in this study show that these phytocompounds could emerge as promising therapeutic inhibitors for the Mpro of SARS-CoV-2. However, urgent trials should be conducted to validate this outcome.Communicated by Ramaswamy H. Sarma.

In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data

BACKGROUNDS AND OBJECTIVES

In silico methods which can generate high-quality physiologically based pharmacokinetic (PBPK) models for arbitrary drug candidates are greatly needed to select developable drug candidates that escape drug attrition because of the poor pharmacokinetic profile. The purpose of this study is to develop a novel protocol to preliminarily predict the concentration profile of a target drug based on the PBPK model of a structurally similar template drug by combining two software platforms for PBPK modeling, the SimCYP simulator and ADMET Predictor.

METHODS

The method was evaluated by utilizing 13 drug pairs from 18 drugs in the built-in database of the SimCYP software. All drug pairs have Tanimoto scores (TS) no less than 0.5. As each drug in a drug pair can serve as both target and template, 26 sets were studied in this work. Three versions (V1, V2 and V3) of models for the target drug were constructed by replacing the corresponding parameters of the template drug step by step with those predicted by ADMET Predictor for the target drug. V1 represents the replacement of molecular weight (MW), V2 includes the replacement of parameter MW, fraction unbound in plasma (f_u), blood-to-plasma partition ratio (B/P), logarithm of the octanol-buffer partition coefficient (log P_o:w) and acid dissociation constant (pK_a). In V3, all above-mentioned parameters as well as human jejunum effective permeability (P_eff), V_d and cytochrome P450 (CYP) metabolism parameters (K_m, V_max or CL_int) are modified. Normalized root mean square error (NRMSE) was used for the evaluation of the model performance.

RESULTS

We found that the performance of the three versions of the models depends on structural similarity of the drug pairs. For Group I drug pairs (TS ≤ 0.7), V2 and V3 performed better than V1 in terms of NRMSE; for Group II drug pairs (0.7 < TS ≤ 0.9), 8 out of 10 V3 models had NRMSE < 0.2, the cutoff we applied to judge whether the simulated concentration-time (C-T) curve was satisfactory or not. V3 outperformed the V1 and V2 versions. For the two drug pairs belonging to Group III (TS > 0.9), V2 outperformed V1 and V3, suggesting more unnecessary replacement can lower the performance of PBPK models. We also investigated how the prediction accuracy of ADMET Predictor as well as its collaboration with SimCYP influences the quality of PBPK models constructed using SimCYP.

CONCLUSION

In conclusion, we generated practical guidance on applying two mainstream software packages, ADMET Predictor and SimCYP, to construct PBPK models for drugs or drug candidates that lack ADME parameters in model construction.

A latent variable approach to account for correlated inputs in global sensitivity analysis

In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol’s GSA assuming no correlations, Sobol’s GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol’s method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed.

Inter-compound and Intra-compound Global Sensitivity Analysis of a Physiological Model for Pulmonary Absorption of Inhaled Compounds

In recent years, global sensitivity analysis (GSA) has gained interest in physiologically based pharmacokinetics (PBPK) modelling and simulation from pharmaceutical industry, regulatory authorities, and academia. With the case study of an in-house PBPK model for inhaled compounds in rats, the aim of this work is to show how GSA can contribute in PBPK model development and daily use. We identified two types of GSA that differ in the aims and, thus, in the parameter variability: inter-compound and intra-compound GSA. The inter-compound GSA aims to understand which are the parameters that mostly influence the variability of the metrics of interest in the whole space of the drugs' properties, and thus, it is useful during the model development. On the other hand, the intra-compound GSA aims to highlight how much the uncertainty associated with the parameters of a given drug impacts the uncertainty in the model prediction and so, it is useful during routine PBPK use. In this work, inter-compound GSA highlighted that dissolution- and formulation-related parameters were mostly important for the prediction of the fraction absorbed, while the permeability is the most important parameter for lung AUC and MRT. Intra-compound GSA highlighted that, for all the considered compounds, the permeability was one of the most important parameters for lung AUC, MRT and plasma MRT, while the extraction ratio and the dose for the plasma AUC. GSA is a crucial instrument for the quality assessment of model-based inference; for this reason, we suggest its use during both PBPK model development and use.

Variance based global sensitivity analysis of physiologically based pharmacokinetic absorption models for BCS I-IV drugs

Regulatory agencies have a strong interest in sensitivity analysis for the evaluation of physiologically-based pharmacokinetic (PBPK) models used in pharmaceutical research and drug development and regulatory submissions. One of the applications of PBPK is the prediction of fraction absorbed and bioavailability for drugs following oral administration. In this context, we performed a variance based global sensitivity analysis (GSA) on in-house PBPK models for drug absorption, with the aim of identifying key parameters that influence the predictions of the fraction absorbed and the bioavailability for neutral, acidic and basic compounds. This analysis was done for four different classes of drugs, defined according to the Biopharmaceutics Classification System, differentiating compounds by permeability and solubility. For class I compounds (highly permeable, highly soluble), the parameters that mainly influence the fraction absorbed are related to the formulation properties, for class II compounds (highly permeable, lowly soluble) to the dissolution process, for class III (lowly permeable, highly soluble) to both absorption process and formulation properties and for class IV (lowly permeable, lowly soluble) to both absorption and dissolution processes. Considering the bioavailability, the results are similar to those for the fraction absorbed, with the addition that parameters related to gut wall and liver clearance influence as well the predictions. This work aimed to give a demonstration of the GSA methodology and highlight its importance in improving our understanding of PBPK absorption models and in guiding the choice of parameters that can safely be assumed, estimated or require data generation to allow informed model prediction.

Does Size Really Matter? Probing the Efficacy of Structural Reduction in the Optimization of Bioderived Compounds - A Computational "Proof-of-Concept"

Over the years, numerous synthetic approaches have been utilized in drug design to improve the pharmacological properties of naturally derived compounds and most importantly, minimize toxic effects associated with their transition to drugs. The reduction of complex bioderived compounds to simpler bioactive fragments has been identified as a viable strategy to develop lead compounds with improved activities and minimal toxicities. Although this ‘reductive’ strategy has been widely exemplified, underlying biological events remain unresolved, hence the unanswered question remains how does the fragmentation of a natural compound improve its bioactivity and reduce toxicities? Herein, using a combinatorial approach, we initialize a computational “proof-of- concept” to expound the differential pharmacological and antagonistic activities of a natural compound, Anguinomycin D, and its synthetic fragment, SB640 towards Exportin Chromosome Region Maintenance 1 (CRM1). Interestingly, our findings revealed that in comparison with the parent compound, SB640 exhibited improved pharmacological attributes, while toxicities and off-target activities were relatively minimal. Moreover, we observed that the reduced size of SB640 allowed ‘deep access’ at the Nuclear Export Signals (NES) binding groove of CRM1, which favored optimal and proximal positioning towards crucial residues while the presence of the long polyketide tail in Anguinomycin D constrained its burial at the hydrophobic groove. Furthermore, with regards to their antagonistic functions, structural inactivation (rigidity) was more pronounced in CRM1 when bound by SB640 as compared to Anguinomycin D. These findings provide essential insights that portray synthetic fragmentation of natural compounds as a feasible approach towards the discovery of potential leads in disease treatment.