Reengineering the pharmaceutical industry by crash-testing molecules

The Use of Global Sensitivity Analysis to Assess the Oral Absorption of Weakly Basic Compounds: A Case Example of Dipyridamole

OBJECTIVE

To utilize the global system analysis (GSA) in oral absorption modeling to gain a deeper understanding of system behavior, improve model accuracy, and make informed decisions during drug development.

METHODS

GSA was utilized to give insight into which drug substance (DS), drug product (DP), and/or physiological parameter would have an impact on peak plasma concentration (Cmax) and area under the curve (AUC) of dipyridamole as a model weakly basic compound. GSA guided the design of in vitro experiments and oral absorption risk assessment using FormulatedProducts v2202.1.0. The solubility and precipitation profiles of dipyridamole in different bile salt concentrations were measured. The results were then used to build a mechanistic oral absorption model.

RESULTS

GSA warranted further investigation into the precipitation kinetics and its link to the levels of bile salt concentrations. Mechanistic modeling studies demonstrated that a precipitation-integrated modeling approach appropriately predicted the mean plasma profiles, Cmax, and AUC from the clinical studies.

CONCLUSIONS

This work shows the value of GSA utilization in early development to guide in vitro experimentation and build more confidence in identifying the critical parameters for the mathematical models.

Molecular modelling of SdiA protein by selected flavonoid and terpenes compounds to attenuate virulence in Klebsiella pneumoniae

Klebsiella pneumoniae is one of the perturbing multidrug resistant (MDR) and ESKAPE pathogens contributing to the mounting morbidity, mortality and extended rate of hospitalization. Its virulence, often regulated by quorum sensing (QS) reinforces the need to explore alternative and prospective antivirulence agents, relatively from plants secondary metabolites. Computer aided drug discovery using molecular modelling techniques offers advantage to investigate prospective drugs to combat MDR pathogens. Thus, this study employed virtual screening of selected terpenes and flavonoids from medicinal plants to interrupt the QS associated SdiA protein in K. pneumoniae to attenuate its virulence. 4LFU was used as a template to model the structure of SdiA. ProCheck, Verify3D, Ramachandran plot scores, and ProSA-Web all attested to the model's good quality. Since SdiA protein in K. pneumoniae leads to the expression of virulence, 31 prospective bioactive compounds were docked for antagonistic potential. The stability of the protein-ligand complex, atomic motions and inter-atomic interactions were further investigated through molecular dynamics simulations (MDS) at 100 ns production runs. The binding free energy was estimated using the molecular mechanics/poisson-boltzmann surface area (MM/PB-SA). Furthermore, the drug-likeness properties of the studied compounds were validated. Docking studies showed phytol possesses the highest binding affinity (-9.205 kcal/mol) while glycitein had -9.752 kcal/mol highest docking score. The MDS of the protein in complex with the best-docked compounds revealed phytol with the highest binding energy of -44.2625 kcal/mol, a low root-mean-square deviation (RMSD) value of 1.54 Å and root-mean-square fluctuation (RMSF) score of 1.78 Å. Analysis of the drug-likeness properties prediction and bioavailability of these compounds revealed their conformed activity to lipinski's rules with bioavailability scores of 0.55 F. The studied terpenes and flavonoids compounds effectively thwart SdiA protein, therefore regulate inter- or intra cellular communication and associated in virulence Enterobacteriaceae, serving as prospective antivirulence drugs.Communicated by Ramaswamy H. Sarma.

The inhibitory effect of 6-gingerol and cisplatin on ovarian cancer and antitumor activity: In silico, in vitro, and in vivo

Background

Epithelial ovarian cancer is very common in women and causes hundreds of deaths per year worldwide. Chemotherapy drugs including cisplatin have adverse effects on patients' health. Complementary treatments and the use of herbal medicines can help improve the performance of medicine. 6-Gingerol is the major pharmacologically active component of ginger. In this study, we compared the effects of 6-gingerol, cisplatin, and their combination in apoptotic and angiogenetic activities in silico, in test tubes, and in in vivo assays against two ovarian cancer cell lines: OVCAR-3 and human umbilical vein endothelial cells (HUVECs).

Methods

The drug-treated cell lines were evaluated for their cytotoxicity, cell cycle, and apoptotic and angiogenetic gene expression changes.

Results

The proportion of apoptosis treated by 6-gingerol coupled with cisplatin was significantly high. In the evaluation of the cell cycle, the combination therapy also showed a significant promotion of a higher extent of the S sequence. The expression of p53 level, Caspase-8, Bax, and Apaf1 genes was amplified again with combination therapy. Conversely, in both cell lines, the cumulative drug concentrations reduced the expression of VEGF, FLT1, KDR, and Bcl-2 genes. Similarly, in the control group, combination treatment significantly decreased the expression of VEGF, FLT1, KDR, and Bcl-2 genes in comparison to cisplatin alone.

Conclusions

The findings of the present study demonstrated that the cisplatin and 6-gingerol combination is more effective in inducing apoptosis and suppressing the angiogenesis of ovarian cancer cells than using each drug alone.

In silico and in vitro screening to identify structurally diverse non-azole CYP51 inhibitors as potent antifungal agent

The problem of resistance to azole class of antifungals is a serious cause of concern to the medical fraternity and thus there is an urgent need to identify non-azole scaffolds with high affinity for lanosterol 14α-demethylase (CYP51). In view of this we have attempted to identify novel non-azole CYP51 inhibitors through the application of pharmacophore based virtual screening and in vitro evaluation. A rigorously validated pharmacophore model comprising of 2 hydrogen bond acceptor and 2 hydrophobic features has been developed and used to mine NCI database. Out of 265 retrieved hits, NSC 1215 and 1520 have been chosen on the basis of Lipinski's rule of five, fit and estimated values. Both the hits were docked into the active site of CYP51. In view of high fit value and CDocker score, NSC 1215 and 1520 have been subjected to in vitro microbiological assay. The result reveals that NSC 1215 and 1520 are active against Candida albicans, Candida parapsilosis, Candida tropicalis, and Aspergillus niger. In addition to this the absorption characteristics of both the hits have also been determined using the rat sac technique and permeation in order of NSC 1520>NSC 1215 has been observed.

Virtual target screening: validation using kinase inhibitors

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term "Virtual Target Screening (VTS)", a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here, we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.

Integration of dissolution into physiologically-based pharmacokinetic models III: PK-Sim®

OBJECTIVES

In-silico methods are a cost-effective possibility to support decision making at different stages of the drug development process. Among the various computational methods available, physiologically-based pharmacokinetic (PBPK) modelling represents a well-established tool for mechanistically predicting the pharmacokinetics of drugs and drug candidates. PK-Sim, a component of the Computational Systems Biology Software Suite of Bayer Technology Services GmbH (Leverkusen, Germany) is a commercial PBPK software tool. It is based on a generic model structure for typical animal species from mice to monkey and humans, and allows simultaneous simulation of drug liberation, absorption, distribution, metabolism, and excretion in one model. In this study PK-Sim has been used for the prediction of the in-vivo pharmacokinetics of drugs with a particular focus on the integration of dissolution properties and, due to its leading role in the drug development process, for the performance of different dosage forms administered via the oral route.

METHODS

Three real life case studies have been presented to exemplify the benefits of using PBPK absorption modelling.

KEY FINDINGS

In the first example, the in-vivo dissolution rate was directly predicted from the physical properties of different particle formulations using a mechanistic dissolution model of the Noyes-Whitney type. In the second case study, the PBPK tool was successfully used to predict the food effect in humans based on data obtained in Beagle dogs. In the third example, the utilization of the software for the support of the development of a combined immediate release-controlled release formulation has been described.

CONCLUSIONS

Future perspectives of the use of PBPK modelling have been discussed, with a special focus on the integration of in-vitro dissolution data into PBPK models for oral and non-oral administration of drugs.

Interaction of 5-(2, 4-dimethylbenzyl) pyrrolidin-2-one with selected antifungal drug target enzymes by in silico molecular docking studies

Currently the criteria used for selecting optimal new antifungal drug candidates include inhibitors of fungal cell wall biosynthesis, essential reaction and pathways. In silico approach resulting in the identification of essential reactions and pathways spreads across several parts of metabolism. The aim of the present study was to study the interaction of the isolated anti-Aspergillus compound, 5-(2, 4-dimethylbenzyl) pyrrolidin-2-one (DMBPO) from a novel Streptomyces VITSVK5 spp. with 6 selected antifungal drug target enzymes by in silico molecular docking approach. The compound DMBPO showed minimum binding energy (-6.66 kcal/mol) with 14 alpha-sterol demethylase (cyp51), (-5.65 kcal/mol) with Rubythrine, and (-4.43 kcal/mol) with β-1-3 Glucan binding protein. Two enzymes 14 alpha-sterol demethylase (cyp51) and β-1-3 Glucan were reported to be mostly responsible for drug resistance in Aspergillus species. The compound DMBPO interacted with several amino acid residues, of which leucine was found to be common among all the target enzymes for protein and hydrogen bond formation. Our results suggest that DMBPO could target Aspergillus fungal proteins to exhibit anti-Aspergillus activity in drug resistant strains.

Intersectional innovation in biomarker development for patient-centric medicine

The pharmaceutical and healthcare industries are being revolutionized by the use of genomics, proteomics, metabolomics, bioinformatics and molecular imaging. Patient friendly diagnosis, treatment and disease management options that utilize the combination of these technologies are currently in development. New innovations in pharmaceutical advancement are taking place at the intersection of these technologies, and will be coupled with societal changes as we move to a fully networked and individual-centric consumer base. Numerous examples of the combinations of molecular characterization technologies aimed at better preclinical and clinical disease understanding, diagnosis and treatment are highlighted that are ideally situated to generate the intersectional innovation that drives healthcare advancement. The true value in patient-centric medicine will only be realized as the improved molecular characterization of disease provided by these technologies is integrated across platforms that operate directly in the patient and care provider space to provide a comprehensive view of health. Molecular profiling and imaging technologies must become fully integrated and amenable for patient and physician use in a networked environment that can provide a personal health avatar approach to medicine.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Methods for Predicting Human Drug Metabolism

Drug metabolism information is a necessary component of drug discovery and development. The key issues in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism. Methods for predicting human drug metabolism from in vitro and computational methodologies and determining relationships between the structure and metabolic activity of molecules are also critically important for understanding potential drug interactions and toxicity. There are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have their own limitations. It is apparent that few of the computational tools for metabolism prediction alone provide the major integrated functions needed to assist in drug discovery. Similarly the different in vitro methods for human drug metabolism themselves have implicit limitations. The utilization of these methods for pharmaceutical and other applications as well as their integration is discussed as it is likely that hybrid methods will provide the most success.

In search of sustainability: process R&D in light of current pharmaceutical industry challenges

Is there a need for a paradigm shift in the pharmaceutical industry? Many researchers think so and take as examples the eroding corporate reputation, a regulatory environment that is harsher than ever, and the request for cheaper drugs from patient organizations and authorities. Process R&D, which interfaces medicinal chemistry and production, has taken on this challenge by increasing the delivery focus early on to ensure timely availability of desired compounds. The quest for lower costs of goods has forced the design of best synthetic routes that, given the molecular complexity, often lead to catalytic methodologies. Applying these methodologies will enable not only the cost element, but also the increasingly important aspects of environmental friendliness, and atom and stage efficiency, to be addressed.

Bowel gas explosion during argon plasma coagulation

The advent of multiple high-throughput technologies has brought drug discovery round almost full circle, from pharmacological testing of compounds in vivo to engineered molecular target assays and back to integrated phenotypic screens in cells and organisms. In the past, primary screens to identify new pharmacological agents involved administering compounds to an animal and monitoring a pharmacologic endpoint. For example, antihypertensive agents were identified by dosing spontaneously hypertensive rats with compounds and observing whether their blood pressure dropped. In taking this phenomenological approach, scientists were focused on the final goal, in this example lowering of blood pressure, rather than developing an understanding of the target, or targets, the compounds were impacting. With the evolution of rational target-based approaches, scientists were able to study the direct interaction of compounds with their intended targets, expecting that this would lead to more-selective and safer therapeutics. With the industrialization of screening, referred to as HTS, hundreds of thousands of compounds were screened in robot-driven assays against targets of interest (with this goal in mind). However, an unintentional outcome of the migration from in vivo primary screens to highly target-specific HTS assays was a reduction in biological context caused by the separation of the target from other cellular proteins and processes that might impact its function. Recognition of the potential consequences of this over-simplification drove the modification of HTS processes and equipment to be compatible with cellular assays.