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Examples from Industrial Practice in Lead Development
Wolfgang MusterF. Hoffmann-La Roche Ltd.
Areas
Computer-Aided Molecular Modeling (CAMM) *
Absorption, Distribution, Metabolism and Excretion (ADME) – Physicochemical Properties
Predictive ToxicologyGenotoxicity/CarcinogenicityPhospholipidosisGenotoxic impurities
* Alternative terms applied to this area:Computer-Aided Drug Design (CADD)Computational Drug Design (CDD)Computer-Aided Molecular Design (CAMD)Rational Drug DesignIn silico Drug DesignComputer-Aided Rational Drug DesignComputer-Aided Drug Discovery and Development (CADDD)Cheminformatics and Molecular ModelingSustainable Pharmacy, Osnabrück 2008
Areas
DrugDrugCandidateCandidate
ADMEProperties
SafetyProfileEfficacy
Sustainable Pharmacy, Osnabrück 2008
Sustainable Pharmacy, Osnabrück 2008
Safety42%
Efficacy28%
Business22%
ADME8%
PredictiveToxicology
In silico ADME
Bettertarget validation
Failure reasons
Stahl et al. (2006) Drug Discover Today 11(7-8): 326-33.Kapetanovic (2008) Chem-Biol Interactions 171: 165-76.
Remark: 3D-receptor modeling for prediction of potential side effects are presently devised (Vedani et al.)
Sustainable Pharmacy, Osnabrück 2008
Computer-Aided Molecular Modeling (CAMM)
* nature of known ligands,homology to related targets,the size, polarity and shape of binding sites in known target 3D structures,knowledge of the key amino acids modulating selective binding or functional activity
*
Stahl et al. (2006) Drug Discover Today 11(7-8): 326-33.Sustainable Pharmacy, Osnabrück 2008
Computer-Aided Molecular Modeling (CAMM)
Various computational methods, such as
Virtual screeningMany computational techniques are available to compile focused compound sets, with most of them falling under the umbrella term ‘virtual screening’
Fragment-based screening
Fragment screening is an additional ‘focused screening’ technique; small libraries of several hundred to several thousand low molecular weight substances that are screened by direct-binding methods in combination with X-ray crystallography
Chemogenomics search strategies(for target classes without structure information, especially for G-protein-coupled receptors)
Multidimensional similarity paradigm: ligand structure similarity, target sequence similarity and similarity of biological effects are combined. Biological similarity is determined in terms of affinity fingerprints of compounds against a set of targets.
Classic structure-based design (QSARs)
should be seen as multifaceted disciplines contributing to the early drug discovery process.
Fostel, J.Predictive ADME-Tox
2005
Stahl et al. (2006) Drug Discover Today 11(7-8): 326-33.Sustainable Pharmacy, Osnabrück 2008
Predictive ADME – Molecular properties
Optimization of chemical series (quality of leads)
All activities of promising compound classes should focus on multiple ADME–Tox-related parameters in parallel to activity and selectivity
Results of commercially available tools for calculating physicochemical properties and ADME-related parameters have to be interpreted with great care
The use of generic models can only be recommended if they have been validated for a particular project; results of new compounds outside of the training sets can be misleading (ionization constants, lipophilicity and solubility)
Shift in optimization strategy, use of measured values calls for high quality, fast and standardized assays (100–500 compounds per week)
Generally, the aim of a local model is to rank compounds and not to predict the absolute magnitude of an in vivo or in vitro effect
Allows project teams to abandon the classic paradigm of sequential filtering in more complex and expensive models (continuous model building; in vivo spot checks)
Use of in silico tools within toxicology:
In silico prediction of toxic effects at early development stages – before drug candidate selection
Hypothesis generation for structural mechanisms of action
In later stages: first assessment of impurities, degradation products, side products, metabolites,...e.g. structural evaluation of synthesis schemes
In silico prediction systems – Toxicology
Sustainable Pharmacy, Osnabrück 2008
Sustainable Pharmacy, Osnabrück 2008
System name Short description Predicted endpointsClassical QSAR approaches Correlate structural or property descriptors of compounds with
biological activitiesQSARs for various endpoints published
DEREK for Windows Knowledge(rule)-based expert system M/C/SS/I and more (>40)
MCASE(CASE, CASETOX)
Machine-learning approach to identify molecular fragments with a high probability of being associated with an observed biological activity
Available modules: M/C/T/I/H/MTD/BD/AT and more
OncoLogic Knowledge-based expert system, mimicking the decision logic of human experts
C
MDL QSAR QSAR modeling system to establish structure-property relationships, create new calculators and generate new compound libraries
M/C/hERG inhib/AT/LD50
lazar Derives predictions from toxicity data by searching the database for compounds that are similar with respect to a given toxic activity
M/C/H/ET
TOPKAT TOPKAT employs cross-validated QSTR models for assessing various measures of toxicity; each module consists of a specificdatabase
Available modules:M/C/T/LD50/SS/I/ET and more
ToxScope ToxScope correlates toxicity information with structural features of chemical libraries, and creates a data mining system
M/C/I/H/T and more
HazardExpert Knowledge(rule)-based expert system M/C/I/SS/IT/NT
COMPACT COMPACT is a procedure for the rapid identification of potentialcarcinogenicity or toxicities mediated by CYP450s
C and P450-mediated toxicities
PASS Based on the comparison of new structures with structures of well-known biological activity profiles by using MNA structure descriptors
Multiple endpoints
Cerius2 Molecular modeling software with a ADME/Tox tool package provides computational models for the prediction of ADME properties
ADME/H
In silico prediction systems – Summary table
Sustainable Pharmacy, Osnabrück 2008
System name Short description Predicted endpoints
Tox Boxes Modules generated by a machine-learning approach implemented in a fragment-based Advanced Algorithm Builder (AAB)
M/AT/C/LD50 and more
MetaDrug Assessment of toxicity by generating networks around proteins and genes (toxicogenomics platform)
>40 QSAR models for ADME/Toxproperties
DICAS Cascade model with the capability to mine for local correlations in datasets with large number of attributes
C
CADD Computer-aided drug design (CADD) by multi-dimensional QSARs applied to toxicity-relevant targets
Receptor- and CYP450-mediated toxicities, ED
CSGeno Tox QSTR-based package employing electrotopological state indexes, connectivity indexes and shape indices
M
Admensa Interactive QSAR-based system primarily for ADME optimization CT
PreADMET Calculation of important descriptors and neural network for the construction of prediction system
M/C
BfR Decision Support System Rule-based system using physicochemical properties and substructures I and corrosion
M=Mutagenicity, C=Carcinogenicity, SS=Skin Sensitisation, I=Irritancy, H=Hepatotoxicity, T=Teratogenicity, MTD=Maximum Tolerated Dose, LD50=, BD=Biodegradation, AT=Acute Toxicity, ET=Environmental Toxicities, IT=Immunotoxicity, NT=Neurotoxicity, CT=Cardiotoxicity, ED= Endocrine disruption, ADME=Absorption Distribution Metabolism Excretion, QSTR=Quantitative Structure Toxicity Relationship, MNA=Multilevel Neighborhoods of Atoms
Muster et al. (2008) Drug Discovery Today 13/7-8, 303-310.
In silico prediction systems – Summary table continued
Genotoxicity endpoint represented by 139 rules* (51 chromosomal damage*)Carcinogenicity endpoint represented by 54 rules*Irritation (skin, eye and respiratory tract) (33 rules*)Sensitisation (skin and respiratory tract) (76 rules*)Thyroid toxicity, hERG channel inhibition, oestrogenicity, photo-induced effects, neurotoxicity, teratogencity: less well coveredNegative in DfW means: really negative or not covered!
DEREK for Windows (DfW)Deductive Estimation of Risk from Existing Knowledge
DfW is a knowledge-based expert system for the qualitative prediction of toxicity. DfW is not a database system but a rulebase system. Each rule describes relationship between a structural feature (toxicophore) and its associated toxicity.
Sustainable Pharmacy, Osnabrück 2008* DfWV9.0.0
In silico prediction systems – Toxicology
MCASE tries to predict toxicity on the basis of discrete structural fragments found to be statistically relevant to specific biological activity (biophores).The differences between active and inactive molecules are investigated with the help of a so-called ‘learning dataset’, to deduce the attributes or substructures (so-called biophores) responsible for activity. From the frequency with which a particular biophore is identified in all active and all inactive molecules, one can calculate the probability with which this fragment is associated with biological activity.
MultiCASE (MCASE)Multiple Computer Automated Structure Evaluation
Ames modules for each strain +/- rat or hamster S9 availableFour carcinogenicity modules incl. proprietary data male/female rats and mice
Modules and the underlying database have been developed with FDAHigh prediction accuracy of the MCASE modules (mainly based on the unique dataset)
Teratogenicity/Developmental toxicity/Male fertility/Behavioral toxicity in diff species (49 modules)Hepatotoxicity in humans (14 modules)GSH adduct formation (in-house) rat and human microsomesFurther available modules: antibacterial (pharm), ADME, cytotoxicity, ecotoxicity, skin/eye irritations, allergies, enzyme inhibition, biodegradation, bioaccumulation
Sustainable Pharmacy, Osnabrück 2008
In silico prediction systems – Toxicology
Sustainable Pharmacy, Osnabrück 2008
DEREK for Windows (DfW)DEREK is a knowledge-based expert system for the qualitative prediction of toxicity. DEREK is not a database system but a rulebase system. Each rule describes relationship between a structural feature (toxicophore) and its associated toxicity.
METEORMeteor is a computer program that helps scientists who need information about the metabolic fate of chemicals. The program uses expert knowledge rules in metabolism to predict the metabolic fate of chemicals and the predictions are presented in metabolic trees. The only information needed by the program to make its prediction is the molecular structure of the chemical.
VITIC Toxicology DatabaseVitic is a chemically intelligent toxicology database, which can recognise and search for similarities in chemical structures. Vitic is especially useful in (Quantitative) Structure-Activity Relationship (QSAR) modelling.
In silico prediction systems – Toxicology
Sustainable Pharmacy, Osnabrück 2008
MultiCASE (MCASE)MCASE tries to predict toxicity on the basis of discrete structural fragments found to be statistically relevant to specific biological activity (biophores).The differences between active and inactive molecules are investigated with the help of a so-called ‘learning dataset’, to deduce the attributes or substructures (so-called biophores) responsible for activity. From the frequency with which a particular biophore is identified in all active and all inactive molecules, one cancalculate the probability with which this fragment is associated with biological activity.
In silico phospholipidosis tool (CAFCA)In-house tool predicts amphiphilic properties of charged small molecules expresed in terms of free energy of amphiphilicity (DDGAM). Amphiphiliccompounds have the potential to accumulate in lipid bilayers, interfering with the phopholipid metabolism and turnover, therefore causing adverse effects.
In silico phototoxicity predictionPhototoxicity prediction based on chemical structure or chemical structure in combination with measured UV spectra
Further endpoints in developmentPromising results with local models with the potential to be generally applicable (e.g. prediction of hERG channel inhibition, GSH adduct formation)
In silico prediction systems – Toxicology
Sustainable Pharmacy, Osnabrück 2008
Expert vs data-driven (QSAR) systems - Toxicology
Local SARs (project-specific SARs) based on 5 to maximally 30 data points; can be evaluated by eye
(Q)SAR systems will get increasing importance if HCS for more toxicological endpoints are validated and implemented
(Q)SAR systems are normally not used for genotoxicity and/or carcinogenicity at Roche
Commercial systems are predicting well and can be optimizedAcceptance of regulatoriesEstablished for other endpoints (e.g. phototoxicity, phospholipidosis, hERG assay)
(Q)SAR systems might be also helpful, if additional in vitro HCSparameters or cross-reactivities have been measured
DEREK / MCASE analysis
MNT in vitro
Ames micro
Ames GLP
MNT in vitro
MNT in vivo
tbd
ML/TK
Gene mutations
Chromosomalaberration
HCAone or both
required forphase II
Crosscheck VITIC,
METEOR,SciFinder,TOXNET
In silico
(HTS)
In vitro
optimize LI/LO
CCS
RDC1 In vivo
Rodent cancer bioassay
Use of in silico genotoxicity prediction
optimize
On-the-fly Prediction/ClassificationDEREK combined with MCASE
Structural assessments of synthesis
scheme, impurities, metabolites
Sustainable Pharmacy, Osnabrück 2008
Sustainable Pharmacy, Osnabrück 2008
The success of early genotoxicity screeningYear Ames micro
number of positive (incl. weak pos. and inconclusive ones) compounds b
Full Ames (GLP)number of positive (incl. weak pos.
and inconclusive ones) compounds b
1996 - 33 (48 %)
1997 - 25 (37 %)
1998 9 (15 %) 11 (24 %)
1999 5 (11 %) 9 (18 %)
2000 11 (11 %) 5 (20 %)
2001 6 (7 %) 3 (21 %)
2002 7 (9 %) 1 (6 %)
2003 3 (3 %) 0
2004 0 0
2007 2 (1 %) 0
2005 3 (2 %) 0
2006 3 (2 %) 0
2008a 1 (2 %) 0
a until March 2008b expected mutagens, intermediates/reactants and positives results due to impurities excluded
Start of routine in silico
screening
Sustainable Pharmacy, Osnabrück 2008
Phospholipidosis
Drug-induced phospholipidosis is a reversible storage disorder characterized by accumulation of phospholipids within cells, i.e., in the lysosomes
Caused by cationic amphiphilic drugs (CADs) and some cationic hydrophilic drugs (e.g. Aminoglycoside gentamicin)
Drug-induced phospholipidosis is a generalized condition in humans and animals; it may occur in virtually any tissue characterized by accumulation of one, or several classes of phospholipids within the cell
Phospholipidosis may or may not be accompanied by organ toxicity although their association has not been proven (except for gentamicin)
Cationic hydrophilic
Hydrophobicresidues
O
N
N
OO
O
Sustainable Pharmacy, Osnabrück 2008
N
O
O
IO
N
I
pKapKa
Negative
ΔΔGAM >= -6 kJ/molpKa < 6.3
ΔΔGAM < -6 kJ/molpKa >= 6.3
Positive
Free Energyof
Amphiphilicity(ΔΔGAM )
In silico classification of phospholipidosis potential
CAFCA (CAlculated Free energy of Charged Amphiphiles) Fischer, H. et al. (2000) Chimia 54, 640-645.
Sustainable Pharmacy, Osnabrück 2008
Techniques to detect phospholipidosisIn silico tool
From in vivo findings to predictive in vitro assay to HT in silico tool
Calculation for large data set possible
Accessible on the Intranet - optimization of pKa value as well as amphiphilic properties
Identification of “clear positive” chemical series rather than single molecules
Useful in Lead Identification and early Lead Optimization (depends on the indication, potency/dose and duration of treatment)
Overall predictability of the in silico tool is very high for the in vitro assay;in vitro test normally not conducted anymore
Amiodaroneas an example of a cationic amphiphilic drug (CAD)
In silico classification of genotoxic impuritiesIn principle, any impurity that is present below the threshold of qualification (0.15%) needs notto be toxicologically „qualified“ or „characterized“ (ICH)
For a drug of 1 g daily intake this implies that a chronic intake of less than 1.5 μg of an impurityin that drug is considered toxicologically insignificant, however, ICH guidelines do indicate that “lower thresholds (for reporting, identification & qualification) can be appropriate if the impurity is unusually toxic” - but do not give guidance on what this is or how to handle
Synthesis of APIs often involves reactive starting materials, intermediates or process steps; synthesis pathways frequently involve known or suspected genotoxic compounds
Unknown/undetermined low levels of genotoxic impurities may be present (such as e.g. sulfonic acid esters)
Issue not directly addressed in ICH guidelines -> new draft of the EMEA ’guideline on the limits of genotoxic impurities’ with new concept
Clinical developments put on hold, because the synthesis pathways contains intermediateswith alerting structures; Companies were requested to either show that the alertingintermediates are below 1 ppm in the drug or provide data on genotoxicity
Solution: use a generic TTC (Threshold of Toxicological Concern) based on historicalexperience with genotoxic carcinogens; staged TTC taking treatment duration into accout
Sustainable Pharmacy, Osnabrück 2008
Sustainable Pharmacy, Osnabrück 2008
Step 1: Identify and classify structural alerts in parent compound and impurities
Step 2: Establish a qualification strategy
A: Limitation based on structural information, chemistry andanalytical capabilities
B: Testing of “neat“ impurity; limitation based on outcome
C: Testing of spiked material; limitation based on outcome
Step 3: Establish acceptable limits
Proposal of acceptable intake levels without appreciable risk based on dose, duration of use, indication and patient/volunteer population(staged TTC)
In silico classification of genotoxic impurities
Sustainable Pharmacy, Osnabrück 2008
Class 1: Genotoxic
Carcinogens
Class 2: Genotoxic,
Carc unknown
Class 3: Alert –Unrelated to parent
Class 4: Alert – Related to
parent
Class 5: No Alerts
Eliminate Impurity?
Staged TTC
Threshold Mechanism?
No or unknown
PDE(e.g. ICH Q3 appendix
2 reference
Control as an ordinary impurity
Impurity Genotoxic?
1
API Genotoxic2
Yes
Yes
/N
ot te
sted
No
1 Either tested neat or spiked into API and tested up to 250 μg/plate2 If API is positive, risk benefit analysis required3 Quantitative risk assessment to determine ADI
Risk Assess-ment?3
No
No
In silico classification of genotoxic impurities
In silico classification of genotoxic impurities
Sustainable Pharmacy, Osnabrück 2008
BasicResearch
PreclinicalDevelopment
LeadOptimization
LeadIdentification
Targetidentification,assessment
and validation
ClinicalDevelopment
Filing/Approval& Launch
Phase 1 Phase 2 Phase 3
ADME / MolecPropclogP / PSA / cPAMPA / cpKaMetabolic clearance
In silico systems during drug development process
Sustainable Pharmacy, Osnabrück 2008
PredTox 1: DEREK / MCASE / VITIC / METEOR
CAMM
PredTox 2:PL / PhototoxhERG / GSH adducts
Adequately predict complex toxicological endpoints (e.g. hepatotoxicity, cardiotoxicity, nephrotoxicity) – need for standardized high-quality data (Innovative Medicine Initiative)
Design in silico tools to cope with the enormous amount of data generated by new techniques – HTS/HCS, omics, system biology, biomarkers, etc.
Establish closer link from preclinical to clinical development
Future challenges for drug design and early screening
Sustainable Pharmacy, Osnabrück 2008
In silico systems are extensively used during the early phases of drug development until selection of the clinical candidate (e.g. 3D-modeling, expert systems, QSARtools)
Applying in silico and in vitro screening significantly reduced failures in early project phases, increased efficiency and improved thquality of clinical candidates
The number of ADME-Tox in silico and (HTS)-in vitro screens are rapidly increasing
DEREK/MCASE and other commercially available systems are predicting toxicity endpoints like mutagenicity, carcinogenicity, skin sensitisation and irritancy well; in-house optimization is essential for high performance
Further endpoints are less-well covered, mainly due to the lack of comprehensive, high quality and standardized databases
QSAR tools can be established, based on internal standardized datasets, e.g. phospholipidosis, phototoxicity, hERG channel inhibition, GSH adduct formation
Challenge how to predict adequately potential genotoxic impurities from structures in synthesis scheme; further regulations needed?
Conclusions
Sustainable Pharmacy, Osnabrück 2008
Sustainable Pharmacy, Osnabrück 2008
Sustainable Pharmacy, Osnabrück 2008
Sustainable Pharmacy, Osnabrück 2008
“Are you sure, Stan, that a pointy head and a long beak is what makes them fly?”
Sustainable Pharmacy, Osnabrück 2008
Alessandro BrigoStephan Kirchner Edith Brandt
Raymond Schmitt Wolfgang HeringJoelle MullerSabine Marget-MullerNicole Helt
Lutz MüllerHolger FischerManfred Kansy
Flavio CrameriLaura Suter-DickThomas WeiserThomas Singer
Acknowledgements