Multivariate Modeling of Cytochrome P450 Enzymes for 4- Aminoquinoline Antimalarial Analogues using Genetic- Algorithms Multiple Linear Regression

Purpose: To develop QSAR modeling of the inhibition of cytochrome P450s (CYPs) by chloroquine and a new series of 4-aminoquinoline derivatives in order to obtain a set of predictive in-silico models using genetic algorithms-multiple linear regression (GA-MLR) methods.Methods: Austin model 1 (AM1) semi-empirical quantum chemical calculation method was used to find the optimum 3D geometry of the studied molecules. The relevant molecular descriptors were selected by genetic algorithm-based multiple linear regression (GA-MLR) approach. In silico predictive models were generated to predict the inhibition of CYP 2B6, 2C9, 2C19, 2D6, and 3A4 isoforms using a set of descriptors.Results: The results obtained demonstrate that our model is capable of predicting the potential of new drug candidates to inhibit multiple CYP isoforms. A cross-validated Q2 test and external validation showed that the models were robust. By inspection of R2pred, and RMSE test sets, it can be seen that the predictive ability of the different CYP models varies considerably.Conclusion: Apart from insights into important molecular properties for CYP inhibition, the findings may also guide further investigations of novel drug candidates that are unlikely to inhibit multiple CYP sub-types.Keywords: Antimalarial, Chloroquine, Cytochrome P450, Genetic algorithm-based multiple linear regression, QSAR

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.JK, MJW, JT, PJB, AB and RCG thank Unilever for funding

Field-based Proteochemometric Models Derived from 3D Protein Structures : A Novel Approach to Visualize Affinity and Selectivity Features

Designing drugs that are selective is crucial in pharmaceutical research to avoid unwanted side effects. To decipher selectivity of drug targets, computational approaches that utilize the sequence and structural information of the protein binding pockets are frequently exploited. In addition to methods that rely only on protein information, quantitative approaches such as proteochemometrics (PCM) use the combination of protein and ligand descriptions to derive quantitative relationships with binding affinity. PCM aims to explain cross-interactions between the different proteins and ligands, hence facilitating our understanding of selectivity. The main goal of this dissertation is to develop and apply field-based PCM to improve the understanding of relevant molecular interactions through visual illustrations. Field-based description that depends on the 3D structural information of proteins enhances visual interpretability of PCM models relative to the frequently used sequence-based descriptors for proteins. In these field-based PCM studies, knowledge-based fields that explain polarity and lipophilicity of the binding pockets and WaterMap-derived fields that elucidate the positions and energetics of water molecules are used together with the various 2D / 3D ligand descriptors to investigate the selectivity profiles of kinases and serine proteases. Field-based PCM is first applied to protein kinases, for which designing selective inhibitors has always been a challenge, owing to their highly similar ATP binding pockets. Our studies show that the method could be successfully applied to pinpoint the regions influencing the binding affinity and selectivity of kinases. As an extension of the initial studies conducted on a set of 50 kinases and 80 inhibitors, field-based PCM was used to build classification models on a large dataset (95 kinases and 1572 inhibitors) to distinguish active from inactive ligands. The prediction of the bioactivities of external test set compounds or kinases with accuracies over 80% (Matthews correlation coefficient, MCC: ~0.50) and area under the ROC curve (AUC) above 0.8 together with the visual inspection of the regions promoting activity demonstrates the ability of field-based PCM to generate both predictive and visually interpretable models. Further, the application of this method to serine proteases provides an overview of the sub-pocket specificities, which is crucial for inhibitor design. Additionally, alignment-independent Zernike descriptors derived from fields were used in PCM models to study the influence of protein superimpositions on field comparisons and subsequent PCM modelling.Lääketutkimuksessa selektiivisten lääkeaineiden suunnittelu on ratkaisevan tärkeää haittavaikutusten välttämiseksi. Kohdeselektiivisyyden selvittämiseen käytetään usein tietokoneavusteisia menetelmiä, jotka hyödyntävät proteiinien sitoutumiskohtien sekvenssi- ja rakennetietoja. Proteiinilähtöisten menetelmien lisäksi kvantitatiiviset menetelmät kuten proteokemometria (proteochemometrics, PCM) yhdistävät sekä proteiinin että ligandin tietoja muodostaessaan kvantitatiivisen suhteen sitoutumisaffiniteettiin. PCM pyrkii selittämään eri proteiinien ja ligandien vuorovaikutuksia ja näin auttaa ymmärtämään selektiivisyyttä. Väitöstutkimuksen tavoitteena oli kehittää ja hyödyntää kenttäpohjaista proteokemometriaa, joka auttaa ymmärtämään relevantteja molekyylitasoisia vuorovaikutuksia visuaalisen esitystavan kautta. Proteiinin kolmiulotteisesta rakenteesta riippuva kenttäpohjainen kuvaus helpottaa PCM-mallien tulkintaa, etenkin usein käytettyihin sekvenssipohjaisiin kuvauksiin verrattuna. Näissä kenttäpohjaisissa PCM-mallinnuksissa käytettiin tietoperustaisia sitoutumistaskun polaarisuutta ja lipofiilisyyttä kuvaavia kenttiä ja WaterMap-ohjelman tuottamia vesimolekyylien sijaintia ja energiaa havainnollistavia kenttiä yhdessä lukuisten ligandia kuvaavien 2D- ja 3D-deskriptorien kanssa. Malleja sovellettiin kinaasien ja seriiniproteaasien selektiivisyysprofiilien tutkimukseen. Tutkimuksen ensimmäisessä osassa kenttäpohjaista PCM-mallinnusta sovellettiin proteiinikinaaseihin, joille selektiivisten inhibiittorien suunnittelu on haastavaa samankaltaisten ATP sitoutumistaskujen takia. Tutkimuksemme osoitti menetelmän soveltuvan kinaasien sitoutumisaffiniteettia ja selektiivisyyttä ohjaavien alueiden osoittamiseen. Jatkona 50 kinaasia ja 80 inhibiittoria käsittäneelle alkuperäiselle tutkimukselle rakensimme kenttäpohjaisia PCM-luokittelumalleja suuremmalle joukolle kinaaseja (95) ja inhibiittoreita (1572) erotellaksemme aktiiviset ja inaktiiviset ligandit toisistaan. Ulkoisen testiyhdiste- tai testikinaasijoukon bioaktiivisuuksien ennustaminen yli 80 % tarkkuudella (Matthews korrelaatiokerroin, MCC noin 0,50) ja ROC-käyrän alle jäävä ala (AUC) yli 0,8 yhdessä aktiivisuutta tukevien alueiden visuaalisen tarkastelun kanssa osoittivat kenttäpohjaisen PCM:n pystyvän tuottamaan sekä ennustavia että visuaalisesti ymmärrettäviä malleja. Tutkimuksen toisessa osassa metodin soveltaminen seriiniproteaaseihin tuotti yleisnäkemyksen sitoutumistaskun eri osien spesifisyyksistä, mikä on ensiarvoisen tärkeää inhibiittorien suunnittelulle. Lisäksi kentistä johdettuja, proteiinien päällekkäinasettelusta riippumattomia Zernike-deskriptoreita hyödynnettiin PCM-malleissa arvioidaksemme proteiinien päällekkäinasettelun vaikutusta kenttien vertailuun ja sen jälkeiseen PCM-mallinnukseen

Structural evidence of quercetin multi-target bioactivity:A reverse virtual screening strategy

The ubiquitous flavonoid quercetin is broadly recognized for showing diverse biological and health-promoting effects, such as anti-cancer, anti-inflammatory and cytoprotective activities. The therapeutic potential of quercetin and similar compounds for preventing such diverse oxidative stress-related pathologies has been generally attributed to their direct antioxidant properties. Nevertheless, accumulated evidence indicates that quercetin is also able to interact with multiple cellular targets influencing the activity of diverse signaling pathways. Even though there are a number of well-established protein targets such as phosphatidylinositol 3 kinase and xanthine oxidase, there remains a lack of a comprehensive knowledge of the potential mechanisms of action of quercetin and its target space. In the present work we adopted a reverse screening strategy based on ligand similarity (SHAFTS) and target structure (idTarget, LIBRA) resulting in a set of predicted protein target candidates. Furthermore, using this method we corroborated a broad array of previously experimentally tested candidates among the predicted targets, supporting the suitability of this screening approach. Notably, all of the predicted target candidates belonged to two main protein families, protein kinases and poly [ADP-ribose] polymerases. They also included key proteins involved at different points within the same signaling pathways or within interconnected signaling pathways, supporting a pleiotropic, multilevel and potentially synergistic mechanism of action of quercetin. In this context we highlight the value of quercetin's broad target profile for its therapeutic potential in diseases like inflammation, neurodegeneration and cancer

A review on machine learning approaches and trends in drug discovery

Abstract: Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.Instituto de Salud Carlos III; PI17/01826Instituto de Salud Carlos III; PI17/01561Xunta de Galicia; Ref. ED431D 2017/16Xunta de Galicia; Ref. ED431D 2017/23Xunta de Galicia; Ref. ED431C 2018/4

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Design, synthesis, and structure-activity relationship studies of dual Plasmodium falciparum phosphatidylinositol 4-kinase and cGMP-dependent protein kinase inhibitors

Malaria is a life-threatening disease caused by protists in the genus Plasmodium and transmitted by the female Anopheles mosquito. Amongst five species which infect humans, Plasmodium falciparum (Pf) causes the severest form of the disease. Although significant efforts have been made to reduce the overall impact of malaria in endemic regions, the ever emergence and continuous spread of parasite resistance to available chemotherapeutics, threatens to undermine advances made thus far. In addition, the current portfolio of drugs is non-effective in addressing chemoprotection, transmission blockade and relapse in P. vivax and P. ovale species. Thus, drugs targeting multiple stages of the parasite life cycle and of low risk to resistance, are highly desirable to support malaria elimination and/or eradication efforts. Considering the success of human kinase inhibitors as anti-cancer drugs and the identification of Plasmodium kinases as promising targets for malaria chemotherapy, this study aimed to optimize anti-plasmodium phosphatidylinositol 4-kinase (PI4K) and the cGMP-dependent protein kinase (PKG) inhibitors, based on two distinct chemotypes. Plasmodium PI4K and PKG are validated targets, each with the potential to deliver pan-stage active compounds with potentially moderate to low risk of resistance. Part 1 of this study focused on the repositioning of the oncological clinical Phase-1 mammalian target of rapamycin (mTOR) inhibitor, MLN0128, as a dual Plasmodium PI4K/PKG inhibitor for malaria. MLN0128 was identified by GlaxoSmithKline (GSK) Cellzome facility as a Plasmodium multi-kinase inhibitor with potent PI4K and PKG inhibitory activity. In this study, an in silico-guided structural modification strategy was undertaken towards optimizing dual Plasmodium kinase inhibition and anti-plasmodium activity while also mitigating potency against its oncological human target, mTOR and off-target PI4KIIIb (Figure 1). Arising from this work, analogues equipotent against both the chloroquine sensitive (PfNF54) and multi-drug resistant (PfK1) strains simultaneously targeting PI4K and PKG were identified. Docking studies using a PfPI4K homology model and a PvPKG crystal structure discerned the molecular features responsible for the high affinity of the inhibitors for these Plasmodium targets. Benzyl analogues containing a fluoro or chloro group at the meta or para positions displayed high anti-plasmodium activity with potent PvPI4K inhibition but weak PfPKG inhibition. Notable analogues included 7 (PfNF54 IC50 = 0.029 µM; PvPI4K IC50 = 0.007 µM; PfPKG IC50 > 2 µM) and 35 (PfNF54 IC50 = 0.086 µM; PvPI4K IC50 = 0.008 µM; PfPKG IC50 > 10 µM). Introduction of basic or pyridyl substituents proved important for dual Plasmodium kinase activity as exemplified by the active anti-plasmodium pyridyl analogues 44 (PfNF54 IC50 = 0.104 µM; PvPI4K IC50 = 0.004 µM; PfPKG IC50 = 0.834 µM) and 49 (PfNF54 IC50 = 0.189 µM; PvPI4K IC50 = 0.006 µM; PfPKG IC50 = 0.384 µM). In addition, the two compounds displayed low cytotoxicity against the Chinese Hamster Ovarian cell line, with a favorable selectivity index (CHO; SI > 100), low human ether-a-go-go-related gene (hERG) activity (IC50 > 10 µM) and high metabolic stability against human, rat, and mouse (H/R/M) liver microsomes (> 75% remaining after 30-min incubation). Selected compounds from the series also showed the potential for transmission blockade with specificity for stage IV/V gametocytes (IC50 100 µM). Compounds displayed potent PvPI4K inhibition but weak PfPKG inhibition (IC50 > 1 µM) in enzyme assays. Four compounds, including one sulfoxide analogue, displayed high stability when incubated with H/R/M liver microsomes in microsomal metabolic stability assays. These features also mitigated hERG activity as five analogues tested displayed an IC50 > 10 µM. Ultimately, a front-runner lead compound (86; GS1 16) with high biological activity and a good safety profile (PfNF54/PfK1 = 0.063/0.100 µM; PvPI4K IC50 = 0.003 µM; CHO SI > 793), optimal solubility (195 µM), favorable microsomal metabolic stability (H/R/M = 96/85/88%) and low affinity on the hERG-encoded potassium channel (IC50 = 44.80 µM), was identified for further progression

Development, validation and application of in-silico methods to predict the macromolecular targets of small organic compounds

Computational methods to predict the macromolecular targets of small organic drugs and drug-like compounds play a key role in early drug discovery and drug repurposing efforts. These methods are developed by building predictive models that aim to learn the relationships between compounds and their targets in order to predict the bioactivity of the compounds. In this thesis, we analyzed the strategies used to validate target prediction approaches and how current strategies leave crucial questions about performance unanswered. Namely, how does an approach perform on a compound of interest, with its structural specificities, as opposed to the average query compound in the test data? We constructed and present new guidelines on validation strategies to address these short-comings. We then present the development and validation of two ligand-based target prediction approaches: a similarity-based approach and a binary relevance random forest (machine learning) based approach, which have a wide coverage of the target space. Importantly, we applied a new validation protocol to benchmark the performance of these approaches. The approaches were tested under three scenarios: a standard testing scenario with external data, a standard time-split scenario, and a close-to-real-world test scenario. We disaggregated the performance based on the distance of the testing data to the reference knowledge base, giving a more nuanced view of the performance of the approaches. We showed that, surprisingly, the similarity-based approach generally performed better than the machine learning based approach under all testing scenarios, while also having a target coverage which was twice as large. After validating two target prediction approaches, we present our work on a large-scale application of computational target prediction to curate optimized compound libraries. While screening large collections of compounds against biological targets is key to identifying new bioactivities, it is resource intensive and challenging. Small to medium-sized libraries, that have been optimized to have a higher chance of producing a true hit on an arbitrary target of interest are therefore valuable. We curated libraries of readily purchasable compounds by: i. utilizing property filters to ensure that the compounds have key physicochemical properties and are not overly reactive, ii. applying a similaritybased target prediction method, with a wide target scope, to predict the bioactivities of compounds, and iii. employing a genetic algorithm to select compounds for the library to maximize the biological diversity in the predicted bioactivities. These enriched small to medium-sized compound libraries provide valuable tool compounds to support early drug development and target identification efforts, and have been made available to the community. The distinctive contributions of this thesis include the development and benchmarking of two ligand-based target prediction approaches under novel validation scenarios, and the application of target prediction to enrich screening libraries with biologically diverse bioactive compounds. We hope that the insights presented in this thesis will help push data driven drug discovery forward.Doktorgradsavhandlin

Investigating the role of Gag in protease inhibitor susceptibility amongst West African HIV-1 subtypes

HIV-1 Gag contributes to susceptibility of protease inhibitors (PIs) in the absence of known resistance mutations in the protease gene. For the majority of HIV-infected patients worldwide, PIs are the second, and last-line of therapy. Clinically, only around 20% of individuals who fail PI regimen develop major resistance mutations in protease. We previously showed that full-length Gagprotease-derived phenotypic susceptibility to PIs differed between HIV-1 CRF02_AG and subtype G-infected patients who went on to successfully suppress viral replication versus those who experienced virological failure of boosted lopinavir monotherapy as first-line treatment in a clinical trial. We hypothesised therefore that baseline PI susceptibility by Gag-protease phenotyping could be used to predict treatment outcomes for patients on second line, boosted-PI treatment in the real-world clinical setting in Nigeria, where subtypes CRF02_AG/G dominate the epidemic. We used clinical and demographic data; HIV-1subtype, sex, age, viral load, duration of treatment and baseline CD4 count to match individuals who experienced second-line failure with ritonavir-boosted PI-based ART (‘baseline failures’) to those who achieved virological response (‘baseline successes’) with virological failure defined by viral load <400 copies of HIV-1 RNA/mL by week 48. Using a single replication-cycle assay, we carried out in vitro phenotypic susceptibility testing of patient-derived viruses from these two groups. We found no impact of baseline HIV-1 Gagprotease-derived phenotypic susceptibility on outcomes of PI-based second-line ART, treatment outcome could not be predicted using baseline susceptibility alone. Secondly, we sought to explore the role of mutation in Gag-protease genotypic and phenotypic changes within patients who failed PI-based regimens without known drug resistance-associated protease mutations in order to identify novel determinants of PI resistance. We used longitudinal samples collected at baseline, and at virological failure to explore the role of Gag mutations. Using target enrichment and next-generation sequencing (NGS), followed by haplotype reconstruction and phenotypic drug assays and phylogenetic analysis, we reported for the first time a four-amino acid mutation signature in HIV-1, CRF02_AG matrix (S126del, H127del, T122A and G123E) which confer reduced susceptibility to the PI, lopinavir and atazanavir. Our multi-pronged genotypic and phenotypic approach to document emergence and temporal dynamics of a novel protease inhibitor resistance signature in HIV- 1 matrix domain reveals the interplay between Gag associated resistance and fitness

Structural Cheminformatics for Kinase-Centric Drug Design

Drug development is a long, expensive, and iterative process with a high failure rate, while patients wait impatiently for treatment. Kinases are one of the main drug targets studied for the last decades to combat cancer, the second leading cause of death worldwide. These efforts resulted in a plethora of structural, chemical, and pharmacological kinase data, which are collected in the KLIFS database. In this thesis, we apply ideas from structural cheminformatics to the rich KLIFS dataset, aiming to provide computational tools that speed up the complex drug discovery process. We focus on methods for target prediction and fragment-based drug design that study characteristics of kinase binding sites (also called pockets). First, we introduce the concept of computational target prediction, which is vital in the early stages of drug discovery. This approach identifies biological entities such as proteins that may (i) modulate a disease of interest (targets or on-targets) or (ii) cause unwanted side effects due to their similarity to on-targets (off-targets). We focus on the research field of binding site comparison, which lacked a freely available and efficient tool to determine similarities between the highly conserved kinase pockets. We fill this gap with the novel method KiSSim, which encodes and compares spatial and physicochemical pocket properties for all kinases (kinome) that are structurally resolved. We study kinase similarities in the form of kinome-wide phylogenetic trees and detect expected and unexpected off-targets. To allow multiple perspectives on kinase similarity, we propose an automated and production-ready pipeline; user-defined kinases can be inspected complementarily based on their pocket sequence and structure (KiSSim), pocket-ligand interactions, and ligand profiles. Second, we introduce the concept of fragment-based drug design, which is useful to identify and optimize active and promising molecules (hits and leads). This approach identifies low-molecular-weight molecules (fragments) that bind weakly to a target and are then grown into larger high-affinity drug-like molecules. With the novel method KinFragLib, we provide a fragment dataset for kinases (fragment library) by viewing kinase inhibitors as combinations of fragments. Kinases have a highly conserved pocket with well-defined regions (subpockets); based on the subpockets that they occupy, we fragment kinase inhibitors in experimentally resolved protein-ligand complexes. The resulting dataset is used to generate novel kinase-focused molecules that are recombinations of the previously fragmented kinase inhibitors while considering their subpockets. The KinFragLib and KiSSim methods are published as freely available Python tools. Third, we advocate for open and reproducible research that applies FAIR principles ---data and software shall be findable, accessible, interoperable, and reusable--- and software best practices. In this context, we present the TeachOpenCADD platform that contains pipelines for computer-aided drug design. We use open source software and data to demonstrate ligand-based applications from cheminformatics and structure-based applications from structural bioinformatics. To emphasize the importance of FAIR data, we dedicate several topics to accessing life science databases such as ChEMBL, PubChem, PDB, and KLIFS. These pipelines are not only useful to novices in the field to gain domain-specific skills but can also serve as a starting point to study research questions. Furthermore, we show an example of how to build a stand-alone tool that formalizes reoccurring project-overarching tasks: OpenCADD-KLIFS offers a clean and user-friendly Python API to interact with the KLIFS database and fetch different kinase data types. This tool has been used in this thesis and beyond to support kinase-focused projects. We believe that the FAIR-based methods, tools, and pipelines presented in this thesis (i) are valuable additions to the toolbox for kinase research, (ii) provide relevant material for scientists who seek to learn, teach, or answer questions in the realm of computer-aided drug design, and (iii) contribute to making drug discovery more efficient, reproducible, and reusable