Basic Principles and Practices of Computer-Aided Drug Design

doi:10.1017/CBO9781139021500.024

Chapter 19 - Basic Principles and Practices of Computer-Aided Drug Design

from Section Four - Chemical Genomics Assays and Screens

Published online by Cambridge University Press: 05 June 2012

Denzil Bernard and

Edited by

Haian Fu: Affiliation:
Emory University, Atlanta

Book contents

Get access

Summary

Technological advances in pharmaceutical research during the past few decades have transformed drug discovery and development from empirical trial-and-error methods to development of mechanism-based compounds, often referred to as targeted therapy. In the targeted therapy approach, the scientific endeavor is to find drugs that can act on specific targets, the majority of which are proteins. Functions, or more typically dysfunctions, of these targeted cellular proteins typically underlie diseases. The therapeutic concept assumes that binding of drugs to the target proteins can alter the proteins’ function in the pathological states of cells. A favorable outcome of drug administration is to nullify or at least mitigate the disease. Development of a drug for treatment of a disease requires many resources as well as much time and collaborative effort of scientists from different disciplines, including chemistry, biology, and pharmacology. In the past decade, the computer has emerged as a powerful tool that can facilitate drug discovery and development. The initial step in the process calls for a correlation of the structures of known compounds with their activities in order to begin the search for new classes of molecules with the requisite activity. In recent years, more sophisticated computational molecular modeling methods have been developed and applied to the development of drugs, from initial discovery of hits and lead optimization to prediction of absorption, distribution, metabolism, and excretion (ADME) properties to toxicity (TOX) evaluation. In this chapter, we focus on the well-established computational methods applied to the identification and optimization of lead compounds. We first discuss the computational methodologies currently used in drug discovery. The strategies used for discovery and optimization of novel ligands by combining different computational tools are described next, and we then present case studies in which computational methods have been employed in drug design. We conclude by highlighting the current challenges and future perspectives of computer-aided drug design (CADD).

Information

Type: Chapter
Information: Chemical Genomics , pp. 259 - 278

DOI: https://doi.org/10.1017/CBO9781139021500.024 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Book purchase

Temporarily unavailable

References

Smith, C 2003 Drug target validation: Hitting the targetNature 422 341Google Scholar

Berman, H. MWestbrook, JFeng, ZGilliland, GBhat, T. NWeissig, HShindyalov, I. NBourne, P. E 2000 The Protein Data Bank. Nucl Acids Res 28 235

Eliezer, D 2009 Biophysical characterization of intrinsically disordered proteinsCurr Opin Struct Biol 19 23Google Scholar

http://targetdb.pdb.org/statistics/TargetStatistics.html#redundancy

Hopkins, A. LGroom, C. R 2002 The druggable genomeNat Rev Drug Discov 1 727Google Scholar

Zhang, YSkolnick, J 2004 Automated structure prediction of weakly homologous proteins on a genomic scaleProc Natl Acad Sci USA 101 7594Google Scholar

Zhang, Y 2008 Progress and challenges in protein structure predictionCurr Opin Struct Biol 18 342Google Scholar

Bonneau, RStrauss, C. E. MRohl, C. AChivian, DBradley, PMalmström, LRobertson, TBaker, D 2002 De novo prediction of three-dimensional structures for major protein familiesJ Mol Biol 322 65Google Scholar

Simons, K. TKooperberg, CHuang, EBaker, D 1997 Assembly of protein tertiary structures from fragments with similar local sequences using simulated annealing and bayesian scoring functionsJ Mol Biol 268 209Google Scholar

Bonneau, RBaker, D 2001 Ab Initio Protein Structure Prediction: Progress and ProspectsAnnu Rev Biophys Biomol Struct 30 173Google Scholar

Fiser, ADo, R. K. GSali, A 2000 Modeling of loops in protein structuresProt Sci 9 1753Google Scholar

Eswar, NMarti-Renom, M. AWebb, BMadhusudhan, M. SEramian, DShen, MPieper, USali, A 2007 1

Sali, ABlundell, T. L 1993 Comparative protein modelling by satisfaction of spatial restraintsJMol Biol 234 779Google Scholar

Marti-Renom, M. AStuart, A. CFiser, ASanchez, RMelo, FSali, A 2000 Comparative protein structure modeling of genes and genomesAnn Rev Biophys Biomol Struct 29 291Google Scholar

Kryshtafovych, AFidelis, K 2009 Protein structure prediction and model quality assessmentDrug Disc Today 14 386Google Scholar

Bradley, PMisura, K. M. SBaker, D 2005 Toward High-Resolution de Novo Structure Prediction for Small ProteinsScience 309 1868Google Scholar

Alber, FDokudovskaya, SVeenhoff, L. MZhang, WKipper, JDevos, DSuprapto, AKarni-Schmidt, OWilliams, RChait, B. TRout, M. PSali, A 2007 Determining the architectures of macromolecular assembliesNature 450 683Google Scholar

Sadowski, JGasteiger, J 1993 From atoms and bonds to three-dimensional atomic coordinates: automatic model buildersChem Rev 93 2567Google Scholar

Ihlenfeldt, W. DTakahashi, YAbe, HSasaki, S 1994 Computation and management of chemical properties in CACTVS: An extensible networked approach toward modularity and compatibilityJ Chem Inf Comp Sci 34 109Google Scholar

Irwin, J. JShoichet, B. K 2005 ZINC: A Free Database of Commercially Available Compounds for Virtual ScreeningJ Chem Inf Model 45 177Google Scholar

Oellien, FCramer, JBeyer, CIhlenfeldt, WSelzer, P. M 2006 The impact of tautomer forms on pharmacophore-based virtual screeningJ Chem Inf Model 46 2342Google Scholar

Ewing, T. J. AMakino, SSkillman, AKuntz, I. D 2001 DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databasesJ Comput Aided Mol Des 15 411Google Scholar

Rarey, MKramer, BLengauer, TKlebe, G 1996 A fast flexible docking method using an incremental construction algorithmJ Mol Biol 261 470Google Scholar

Frembgen-Kesner, TElcock, A. H 2006 Computational sampling of a cryptic drug binding site in a protein receptor: Explicit solvent molecular dynamics and inhibitor docking to p38 MAP KinaseJ Mol Biol 359 202Google Scholar

Jones, GWillett, PGlen, R. CLeach, A. RTaylor, R 1997 Development and validation of a genetic algorithm for flexible dockingJ Mol Biol 267 727Google Scholar

Friesner, R. ABanks, J. LMurphy, R. BHalgren, T. AKlicic, J. JMainz, D. TRepasky, M. PKnoll, E. HShelley, MPerry, J. KShaw, D. EFrancis, PShenkin, P. S 2004 Glide: A new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracyJ Med Chem 47 1739Google Scholar

Halgren, T. AMurphy, R. BFriesner, R. ABeard, H. SFrye, L. LPollard, W. TBanks, J. L 2004 Glide: A new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screeningJ Med Chem 47 1750Google Scholar

Belew, R. KMitchell, M 1996 Adaptive Individuals in Evolving Populations: Models and AlgorithmsReading, MassAddison-Wesley

Hart, W. EKammeyer, T. EBelew, R. K 1994 Foundations of Genetic Algorithms IIIWhitley, DVose, MSan Francisco, CalifMorgan Kauffman

Jones, GWillett, PGlen, R. C 1995 Molecular recognition of receptor sites using a genetic algorithm with a description of desolvationJ Mol Biol 245 43Google Scholar

Goldberg, D. E 1989 Genetic Algorithms in Search, Optimization and Machine LearningReading, MassAddison-Wesley

Bemis, G. WKuntz, I. D 1992 A fast and efficient method for 2D and 3D molecular shape descriptionJ Comput Aided Mol Des 6 607Google Scholar

Mark, RMcGann, H. R. ANicholls, AnthonyAndrew Grant, JBrown, Frank K 2003 Gaussian docking functionsBiopolymers 68 76Google Scholar

Hammes, G 2000 Thermodynamics and Kinetics for the Biological SciencesNew YorkJohn Wiley Interscience

Woo, H.-JRoux, B 2005 Calculation of absolute protein-ligand binding free energy from computer simulationsProc Natl Acad Sci USA 102 6825Google Scholar

Kollman, P 1993 Free energy calculations: Applications to chemical and biochemical phenomenaChem Rev 93 2395Google Scholar

Kollman, PMassova, IReyes, CKuhn, BHuo, SChong, LLee, MLee, TDuan, YWang, WDonini, OCieplak, PSrinivasan, JCase, D. ACheatham, T. E 2000 Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum modelsAcc Chem Res 33 889Google Scholar

Aqvist, JMedina, CSamuelsson, J 1994 A new method for predicting binding affinity in computer-aided drug designProt Eng 7 385Google Scholar

Eldridge, M. DMurray, C. WAuton, T. RPaolini, G. VMee, R. P 1997 Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexesJ Comput Aided Mol Des 11 425Google Scholar

Wang, RLai, LWang, S 2002 Further development and validation of empirical scoring functions for structure-based binding affinity predictionJ Comput Aided Mol Des 16 11Google Scholar

Morris, G. MGoodsell, D. SHalliday, R. SHuey, RHart, W. EBelew, R. KOlson, Arthur J 1998 Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy functionJ Comput Chem 19 1639Google Scholar

Gohlke, HHendlich, MKlebe, G 2000 Knowledge-based scoring function to predict protein-ligand interactionsJ Mol Biol 295 337Google Scholar

Yang, C.-YWang, RWang, S 2006 M-Score: A Knowledge-Based Potential Scoring Function Accounting for Protein Atom MobilityJ Med Chem 49 5903Google Scholar

Muegge, IMartin, Y. C 1999 A General and Fast Scoring Function for Protein-Ligand Interactions: A Simplified Potential ApproachJ Med Chem 42 791Google Scholar

Sharp, KNicholls, AFine, RHonig, B 1991 Reconciling the magnitude of the microscopic and macroscopic hydrophobic effectsScience 252 106Google Scholar

Bashford, DCase, D. A 2000 Generalized born models of macromolecular solvation effectsAnn Rev Phys Chem 51 129Google Scholar

Velec, H. F. GGohlke, HKlebe, G 2005 DrugScoreCSD Knowledge-Based Scoring Function Derived from Small Molecule Crystal Data with Superior Recognition Rate of Near-Native Ligand Poses and Better Affinity PredictionJ Med Chem 48 6296Google Scholar

DeWitte, R. SShakhnovich, E. I 1996 SMoG: de novo design method based on simple, fast, and accurate free energy estimates. 1. Methodology and supporting evidenceJ Am Chem Soc 118 11733Google Scholar

John, B. OMitchell, R. A. LAlex, AlexanderThornton, Janet M 1999 BLEEP: potential of mean force describing protein-ligand interactions: I. Generating potentialJ Comput Chem 20 1165Google Scholar

John, B. OMitchell, R. A. LAlex, AlexanderForster, Mark JThornton, Janet M 1999 BLEEP-potential of mean force describing protein-ligand interactions: II. Calculation of binding energies and comparison with experimental dataJ Comput Chem 20 1177Google Scholar

Friesner, R. AMurphy, R. BRepasky, M. PFrye, L. LGreenwood, J. RHalgren, T. ASanschagrin, P. CMainz, D. T 2006 Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexesJ Med Chem 49 6177Google Scholar

Charifson, P. SCorkery, J. JMurcko, M. AWalters, W. P 1999 Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteinsJ Med Chem 42 5100Google Scholar

Cozzini, PKellogg, G. ESpyrakis, FAbraham, D. JCostantino, GEmerson, AFanelli, FGohlke, HKuhn, L. AMorris, G. MOrozco, MPertinhez, T. ARizzi, MSotriffer, C. A 2008 Target flexibility: An emerging consideration in drug discovery and designJ Med Chem 51 6237Google Scholar

Grünberg, RNilges, MLeckner, J 2006 Flexibility and conformational entropy in protein-protein bindingStructure 14 683Google Scholar

Lee, G. MCraik, C. S 2009 Trapping moving targets with small moleculesScience 324 213Google Scholar

Lange, O. FLakomek, N.-AFares, CSchroder, G. FWalter, K. F. ABecker, SMeiler, JGrubmuller, HGriesinger, Cde Groot, B. L 2008 Recognition dynamics up to microseconds revealed from an rdc-derived ubiquitin ensemble in solutionScience 320 1471Google Scholar

Maria I. Zavodszky, L. A. K 2005 Side-chain flexibility in protein-ligand binding: the minimal rotation hypothesisProt Sci 14 1104Google Scholar

Knegtel, R. M. AKuntz, I. DOshiro, C. M 1997 Molecular docking to ensembles of protein structuresJ Mol Biol 266 424Google Scholar

Ferrari, A. MWei, B. QCostantino, LShoichet, B. K 2004 Soft docking and multiple receptor conformations in virtual screeningJ Med Chem 47 5076Google Scholar

Sherman, WDay, TJacobson, M. PFriesner, R. AFarid, R 2006 Novel Procedure for Modeling Ligand/receptor induced fit effectsJ Med Chem 49 534Google Scholar

Carlson, H. AMasukawa, K. MRubins, KBushman, FJorgensen, WLins, RBriggs, JMcCammon, J 2000 Developing a dynamic pharmacophore model for HIV-1 integraseJ Med Chem 43 2100Google Scholar

Lin, J.-HPerryman, A. LSchames, J. RMcCammon, J. A 2002 Computational drug design accommodating receptor flexibility: The relaxed complex schemeJ Am Chem Soc 124 5632Google Scholar

Bowman, A. LNikolovska-Coleska, ZZhong, HWang, SCarlson, H. A 2007 Small molecule inhibitors of the MDM2-p53 interaction discovered by ensemble-based receptor modelsJ Am Chem Soc 129 12809Google Scholar

Klebe, G 2006 Virtual ligand screening: strategies, perspectives and limitationsDrug Dis Today 11 580Google Scholar

Feng, B. YSimeonov, AJadhav, ABabaoglu, KInglese, JShoichet, B. KAustin, C. P 2007 A high-throughput screen for aggregation-based inhibition in a large compound libraryJ Med Chem 50 2385Google Scholar

Lipinski, C. ALombardo, FDominy, B. WFeeney, P. J 1997 Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settingsAdv Drug Del Rev 23 3Google Scholar

Davis, A. MRiley, R. J 2004 Predictive ADMET studies, the challenges and the opportunitiesCurr Opin Chem Biol 8 378Google Scholar

Hammett, L. P 1937 The effect of structure upon the reactions of organic compounds benzene derivativesJ Am Chem Soc 59 96Google Scholar

Hansch, C 1969 A Quantitative Approach to Biochemical Structure-Activity RelationshipsAcc Chem Res 2 232Google Scholar

Cramer, R. DPatterson, D. EBunce, J. D 1988 Comparative molecular-field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteinsJ Am Chem Soc 110 5959Google Scholar

Hopfinger, A. JWang, STokarski, J. SJin, B. QAlbuquerque, MMadhav, P. JDuraiswami, C 1997 Construction of 3D-QSAR models using the 4D-QSAR analysis formalismJ Am Chem Soc 119 10509Google Scholar

Vedani, ADobler, M 2002 5D-QSAR: The key for simulating induced fitJ Med Chem 45 2139Google Scholar

Ehrlich, P 1909 On the present state of chemotherapyBer Der Deut Chem Ges 42 17Google Scholar

Schueler, F. W 1946 Sex hormonal action and chemical constitutionScience 103 221Google Scholar

Beckett, A. H 1959 Stereochemical factors in biological activityFort der Arzneimittelforschung 1959 455Google Scholar

Bernard, DCoop, AMacKerell, A. D 2003 2D conformationally sampled pharmacophore: a ligand-based pharmacophore to differentiate delta opioid agonists from antagonistsJ Am Chem Soc 125 3101Google Scholar

Bernard, DCoop, AMackerell, A. D 2005 Conformationally sampled pharmacophore for peptidic delta opioid ligandsJ Med Chem 48 7773Google Scholar

Bernard, DCoop, AMacKerell, A. D 2007 Quantitative conformationally sampled pharmacophore for delta opioid ligands: reevaluation of hydrophobic moieties essential for biological activityJ Med Chem 50 1799Google Scholar

Shoichet, B. K 2004 Virtual screening of chemical librariesNature 432 862Google Scholar

Taylor, R. DJewsbury, P. JEssex, J. W 2002 A review of protein-small molecule docking methodsJ Comput Aided Mol Des 16 151Google Scholar

Bohm, H. J 1992 The computer program LUDI: a new method for the de novo design of enzyme inhibitorsJ Comput Aided Mol Des 6 61Google Scholar

Wu, Y.-DGellman, S 2008 PeptidomimeticsAcc Chem Res 41 1231Google Scholar

Jorgensen, W. L 2004 The Many Roles of Computation in Drug DiscoveryScience 303 1813Google Scholar

DesJarlais, R. LSeibel, G. LKuntz, I. DFurth, P. SAlvarez, J. COrtiz de Montellano, P. RDeCamp, D. LBabe, L. MCraik, C. S 1990 Structure-based design of nonpeptide inhibitors specific for the human immunodeficiency virus 1 proteaseProc Natl Acad Sci USA 87 6644Google Scholar

DesJarlais, R. LSheridan, R. PSeibel, G. LDixon, J. SKuntz, I. DVenkataraghavan, R 1988 Using shape complementarity as an initial screen in designing ligands for a receptor binding site of known three-dimensional structureJ Med Chem 31 722Google Scholar

Luksch, TChan, N. SBrass, SSotriffer, C. AKlebe, GDiederich, W. E 2008 Computer-aided design and synthesis of nonpeptidic plasmepsin II and IV inhibitorsChemMedChem 3 1323Google Scholar

Mustata, GFollis, A. VHammoudeh, D. IMetallo, S. JWang, HProchownik, E. VLazo, J. SBahar, I 2009 Discovery of novel myc-max heterodimer disruptors with a three-dimensional pharmacophore modelJ Med Chem 52 1247Google Scholar

Kubinyi, H 1998 Structure-based design of enzyme inhibitors and receptor ligandsCurr Opin Drug Dis Dev 1 4Google Scholar

Lu, YNikolovska-Coleska, ZFang, XGao, WShangary, SQiu, SQin, DWang, S 2006 Discovery of a nanomolar inhibitor of the human murine double minute 2 (MDM2)-p53 interaction through an integrated, virtual database screening strategyJ Med Chem 49 3759Google Scholar

Milne, G. WNicklaus, M. CDriscoll, J. SWang, SZaharevitz, D 1994 National cancer institute drug information system 3D databaseJ Chem Inf Comput Sci 34 1219Google Scholar

Voigt, J. HBienfait, BWang, SNicklaus, M. C 2001 Comparison of the NCI open database with seven large chemical structural databasesJ Chem Inf Comput Sci 41 702Google Scholar

Verdonk, M. LCole, J. CHartshorn, M. JMurray, C. WTaylor, R. D 2003 Improved protein-ligand docking using GOLDProteins 52 609Google Scholar

Ding, KLu, YNikolovska-Coleska, ZQiu, SDing, YGao, WStuckey, JKrajewski, KRoller, P. PTomita, YParrish, D. ADeschamps, J. RWang, S 2005 Structure-based design of potent non-peptide MDM2 inhibitorsJ Am Chem Soc 127 10130Google Scholar

Ding, KLu, YNikolovska-Coleska, ZWang, GQiu, SShangary, SGao, WQin, DStuckey, JKrajewski, KRoller, P. PWang, S 2006 Structure-based design of spiro-oxindoles as potent, specific small-molecule inhibitors of the MDM2-p53 interactionJ Med Chem 49 3432Google Scholar

Kussie, P. HGorina, SMarechal, VElenbaas, BMoreau, JLevine, A. JPavletich, N. P 1996 Structure of the MDM2 oncoprotein bound to the p53 tumor suppressor transactivation domainScience 274 948Google Scholar

Holcik, MGibson, HKorneluk, R 2001 XIAP: Apoptotic brake and promising therapeutic targetApoptosis 6 253Google Scholar

Deveraux, Q. LReed, J. C 1999 IAP family proteins-suppressors of apoptosisGenes & Dev 13 239Google Scholar

Salvesen, G. SDuckett, C. S 2002 IAP proteins: blocking the road to death's doorNat Rev Mol Cell Biol 3 401Google Scholar

Wu, GChai, JSuber, T. LWu, J.-WDu, CWang, XShi, Y 2000 Structural basis of IAP recognition by Smac/DIABLONature 408 1008Google Scholar

Liu, ZSun, COlejniczak, E. TMeadows, R. PBetz, S. FOost, THerrmann, JWu, J. CFesik, S. W 2000 Structural basis for binding of Smac/DIABLO to the XIAP BIR3 domainNature 408 1004Google Scholar

Sun, HNikolovska-Coleska, ZYang, C.-YXu, LTomita, YKrajewski, KRoller, P. PWang, S 2004 Structure-based design, synthesis, and evaluation of conformationally constrained mimetics of the second mitochondria-derived activator of caspase that target the X-linked inhibitor of apoptosis protein/caspase-9 interaction siteJ Med Chem 47 4147Google Scholar

Sun, HStuckey, J. ANikolovska-Coleska, ZQin, DMeagher, J. LQiu, SLu, JYang, C.-YSaito, N. GWang, S 2008 Structure-based design, synthesis, evaluation, and crystallographic studies of conformationally constrained smac mimetics as inhibitors of the X-linked inhibitor of apoptosis protein (XIAPJ Med Chem 51 7169Google Scholar

Oost, T. KSun, CArmstrong, R. CAl-Assaad, A.-SBetz, S. FDeckwerth, T. LDing, HElmore, S. WMeadows, R. POlejniczak, E. TOleksijew, AOltersdorf, TRosenberg, S. HShoemaker, A. RTomaselli, K. JZou, HFesik, S. W 2004 Discovery of potent antagonists of the antiapoptotic protein XIAP for the treatment of cancerJ Med Chem 47 4417Google Scholar

Cohen, FAlicke, BElliott, L. OFlygare, J. AGoncharov, TKeteltas, S. FFranklin, M. CFrankovitz, SStephan, J.-PTsui, VVucic, DWong, HFairbrother, W. J 2009 Orally bioavailable antagonists of inhibitor of apoptosis proteins based on an azabicyclooctane scaffoldJ Med Chem 49 1723Google Scholar

Warren, G. LAndrews, C. WCapelli, A.-MClarke, BLaLonde, JLambert, M. HLindvall, MNevins, NSemus, S. FSenger, STedesco, GWall, I. DWoolven, J. MPeishoff, C. EHead, M. S 2006 A critical assessment of docking programs and scoring functionsJ Med Chem 49 5912Google Scholar

Zentgraf, MSteuber, HKoch, CLa Motta, CSartini, SSotriffer, Christoph AKlebe, G 2007 How reliable are current docking approaches for structure-based drug design? lessons from aldose reductaseAngew Chem Int Ed 46 3575Google Scholar

Leach, A. RShoichet, B. KPeishoff, C. E 2006 Prediction of protein-ligand interactions. docking and scoring: successes and gapsJ Med Chem 49 5851Google Scholar

Wells, J. AMcClendon, C. L 2007 Reaching for high-hanging fruit in drug discovery at protein-protein interfacesNature 450 1001Google Scholar

Yang, C.-YWang, S 2006 Recent advances in design of small-molecule ligands to target protein-protein interactionsAnn Rep Comput ChemDavid, C. SElsevier197

Fry, David C 2006 Protein-protein interactions as targets for small molecule drug discoveryPept Sci 84 535Google Scholar

Clackson, TWells, J 1995 A hot spot of binding energy in a hormone-receptor interfaceScience 267 383Google Scholar

Wells, J. A 1996 Binding in the growth hormone receptor complexProc Natl Acad Sci USA 93 1Google Scholar

Accessibility standard: Unknown

Why this information is here

This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.

Accessibility Information

Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.