Computer-Aided Drug Designing (CADD) is a specialized discipline that uses computational methods to simulate drug-receptor interactions
CADD methods are heavily dependent on bioinformatics tools, applications, and databases
CONCEPT OF PHARMACOPHORE
PHARMACOPHORE MAPPING
IDENTIFICATION OF PHARMACOPHORE FEATURE
CONFORMATIONAL SEARCH
INSILICO DRUG DESIGN
VIRTUAL SCREENING
PHARMACOPHORE BASED SCREENING
First introduced by Paul Heritich in 1990
A pharmacophore is an abstract description of molecular features which are necessary for molecular recognition of a ligand by a biological macromolecule.
It is the key features responsible for an activity (eg. Substrates, inhibitors)
A pharmacophore is a representation of generalized molecular features including;
3D (hydrophobic group, chaeged /ionisable group, hydrogen bond donar/ acceptor)
2D (substructure)
1D (physical & biological)
Pharmacophore Mapping is the definition and placement of pharmacophoric features and the alignment techniques used to overlay 3D.
Two somewhat distinct usages:
That substructure of a molecule that is responsible for its pharmacological activity (c.f. chromophore)
A set of geometrical constraints between specific functional groups that enable the molecule to have biological activity
The process of deriving pharmacophore is known as pharmacophore mapping.
Computer-Aided Drug Designing (CADD) is a specialized discipline that uses computational methods to simulate drug-receptor interactions
CADD methods are heavily dependent on bioinformatics tools, applications, and databases
CONCEPT OF PHARMACOPHORE
PHARMACOPHORE MAPPING
IDENTIFICATION OF PHARMACOPHORE FEATURE
CONFORMATIONAL SEARCH
INSILICO DRUG DESIGN
VIRTUAL SCREENING
PHARMACOPHORE BASED SCREENING
First introduced by Paul Heritich in 1990
A pharmacophore is an abstract description of molecular features which are necessary for molecular recognition of a ligand by a biological macromolecule.
It is the key features responsible for an activity (eg. Substrates, inhibitors)
A pharmacophore is a representation of generalized molecular features including;
3D (hydrophobic group, chaeged /ionisable group, hydrogen bond donar/ acceptor)
2D (substructure)
1D (physical & biological)
Pharmacophore Mapping is the definition and placement of pharmacophoric features and the alignment techniques used to overlay 3D.
Two somewhat distinct usages:
That substructure of a molecule that is responsible for its pharmacological activity (c.f. chromophore)
A set of geometrical constraints between specific functional groups that enable the molecule to have biological activity
The process of deriving pharmacophore is known as pharmacophore mapping.
molecular docking its types and de novo drug design and application and softw...GAUTAM KHUNE
This ppt deals with all the aspects related to molecular docking ,its types(rigid ,flexible and manual) and screening based on it and also deals with de novo drug design , various softwares available for docking methodologies and applications for molecular docking in new drug design
SAR versus QSAR, History and development of QSAR, Types of physicochemical
parameters, experimental and theoretical approaches for the determination of
physicochemical parameters such as Partition coefficient, Hammet’s substituent
constant and Taft’s steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR
approaches like COMFA and COMSIA.
The basic aspects of drug discovery starts from target discovery and validation further going to lead identification and optimization. In this particular slide discussion is regarding the target discovery and the tools that have been utilized in this process.
molecular docking its types and de novo drug design and application and softw...GAUTAM KHUNE
This ppt deals with all the aspects related to molecular docking ,its types(rigid ,flexible and manual) and screening based on it and also deals with de novo drug design , various softwares available for docking methodologies and applications for molecular docking in new drug design
SAR versus QSAR, History and development of QSAR, Types of physicochemical
parameters, experimental and theoretical approaches for the determination of
physicochemical parameters such as Partition coefficient, Hammet’s substituent
constant and Taft’s steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR
approaches like COMFA and COMSIA.
The basic aspects of drug discovery starts from target discovery and validation further going to lead identification and optimization. In this particular slide discussion is regarding the target discovery and the tools that have been utilized in this process.
PRESENTED BY: HARSHPAL SINGH WAHI, SHIKHA D. POPALI
USEFUL FOR PHARMACY STUDENTS AND ACADEMICS, INDUSTRIALS FOR MOLECULE DEVELOPMENT, MODELING, DRUG DISCOVERY, COMPUTATIONAL TOOLS, MOLECULAR DOCKING ITS TYPES, FACTORS AFFECTING, DIFFERENT STAGES, QSAR ADVANTAGES, NEED
Fragment screening library workshop (IQPC 2008)Peter Kenny
I also ran a workshop on selection of compounds for fragment screening just before the 2008 IQPC compound library conference and these are the slides I used.
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
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Title: Sense of Taste
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
2. “ The new paradigm, now emerging is that all
the 'genes' will be known (in the sense of
being resident in databases available
electronically), and that the starting "point of a
biological investigation will be theoretical.”
Sir Walter Gilbert
3. Drug Discovery Process
Science 2004, 303, 1813-8
Target
selection
Proof of
efficacy
Clinical trials &
Therapeutics
Genomics
Proteomics
Bioinformatics
Target validation
Assay developments.
Lead identification
HTS and vHTS
In-silico studies
Lead
Discovery
Medicinal
Chemistry
Lead optimization
SAR generation
X-ray Crystallography
Formulation development
In-vitro studies
In-vivo studies
PK/PD refinement
Biomarker identification
Safety studies
Pharmacokinetics
Clinical efficacy
Marketing
5. Structure based drug design involved two steps:
Lead identification: Identification of initial chemical core structure,
which have desired biological effect in moderate potency can be
optimized to superior drug candidate.
Lead optimization: Chemical modification in lead structure by
medicinal chemist in order to improve the biological activity or the
physiochemical properties.
e.g. taxol and optimized drug is paclitaxel.
Similarly, nalidaxic acid is lead candidate and optimized drug is
ciprofloxacin.
Similarly, Morphine is lead candidate and sufentanyl is the superior
anti-nociceptive drug.
6. What is Docking?
Docking attempts to find the “best” matching between two
molecules. i.e. host and guest molecule. Where host is protein and
guest is both ligand and protein.
7. The interaction of a drug with its
receptor is a complex process. Many
factors are involved in the
intermolecular association such as
hydrophobic; Van der Waal’s, hydrogen
bonding and electrostatic forces.
Ligand docking to receptor
Ligand database Target Protein
Molecular docking
Ligand docked into protein
•The process of “DOCKING” can be defined as a fitting of a ligand to
binding sites which tries to mimic the natural course of interaction of
the ligand and its receptor via a lowest energy pathway. Usually the
receptor is kept rigid while the conformation of the drug molecule is
allowed to change. The molecules are physically moved closer to none
another and the preferred docked conformation is minimized
8. Protein-ligand docking
• It Predicts...
• The pose of the molecule in the
binding site
• The binding affinity or a score
representing the strength of
binding
9. Pose vs. binding site
• Binding site (or “active site”)
• the part of the protein where the ligand binds
• generally a cavity on the protein surface
• can be identified by looking at the crystal
structure of the protein bound with a known
inhibitor
• Pose (or “binding mode”)
• The geometry of the ligand in the binding site
• Geometry = location, orientation and
conformation
10. Steps in ligand-protein docking
• Protein preparation:
• Ligand Preparation:
• Definition of binding site and docking methodology:
• Evaluation of binding poses:
• Scoring of poses:
11. 1. Preparation of protein structure
• PDB structures often contain water molecules: In general, all water
molecules are removed except where it is known that they play an important
role in coordinating to the ligand
• PDB structures are missing all hydrogen atoms: So incorporates
H atom.
• An incorrect assignment of protonation states
in the active site will give poor results
• Glutamate, Aspartate have COO- or COOH
where OH is hydrogen bond donor, O- is not
• Histidine is a base and its neutral form has
two tautomers
H
N
H N
R
+
N
H N
R
R
NH
N
12. Preparation of protein structure
• Crystallography gives electron density, not molecular structure
• In poorly resolved crystal structures of proteins, isoelectronic groups can
give make it difficult to deduce the correct structure
• Affects asparagine, glutamine, histidine and affects hydrogen
bonding pattern.
R
O
NH2
R
NH
2
O
R
N
N
N
N
R
13. 2. Ligand Preparation
• A reasonable 3D structure is required as starting point
• During docking, the bond lengths and angles in ligands are held fixed;
only the torsion angles are changed
• The protonation state and tautomeric form of a particular ligand
could influence its hydrogen bonding ability: Either protonate as
expected for physiological pH and use a single tautomer Or generate and dock
all possible protonation states and tautomers, and retain the one with the
highest score OH O
H+
Enol Ketone
14. Docking algorithms: Rigid Docking
• We can classify the various search algorithms for docking based on the type of
bonding such as (covalent docking and non-covalent docking). Further non-covalent
docking could be broadly categorized based on the degrees of freedom of chemical
bonds that they consider in ligand structures and protein.
• Rigid docking: The ligand as well as protein is treated as a rigid structure during the
docking. Only the translational and rotational degrees of freedom are considered.
• To deal with the problem of ligand conformations, a large number of conformations
of each ligand are generated in advance and each is docked separately e.g. FRED
(Fast Rigid Exhaustive Docking), and DOCK
3. Definition of binding site and docking methodology:
15. The DOCK algorithm – Rigid ligand docking
AR Leach, VJ Gillet, An Introduction to Cheminformatics
16. Flexible ligand docking:
• Flexible docking: Conformations of each molecule are generated on-the-fly by the search
algorithm during the docking process and The algorithm can avoid considering
conformations that do not fit. Sometimes they also uses Stochastic search methods:
Stochastic means that they incorporate a incremental degree of randomness to whole
molecule. E.g. Genetic algorithms (GOLD), and Simulated annealing (AutoDock).
• Incremental construction methods:
• These construct conformations of the ligand within the binding site in a series of stages
• First one or more “base fragments” are identified which are docked into the binding site
• The orientations of the base fragment then act as anchors for a systematic
conformational analysis of the remainder of the ligand e.g. : FlexX and GLIDE.
19. Handling protein conformations and flexibility
• Most docking software treats the protein as rigid: Rigid Receptor Approximation.
This approximation may be invalid for a particular protein-ligand complex as...
• the protein may deform slightly to accommodate different ligands (ligand-induced fit)
• protein side chains in the active site may adopt different conformations.
• Some docking programs allow protein side-
chain flexibility: For example, selected side
chains are allowed to undergo torsional rotation
around acyclic bonds.
• Larger protein movements can only be handled
by separate dockings to different protein
conformations e.g. Ensemble docking (e.g.
GOLD 5.0)
20. 4. Evaluation of binding poses
• Typically, protein-ligand docking software consist of two main
components which work together:
1. Search algorithm: Generates a large number of poses of a molecule in the
binding site
2. Scoring function: Calculates a score or binding affinity
for a particular pose
To give:
• The pose of the molecule in the
binding site
• The binding affinity or a score of pose
representing the strength of binding
21. An Effective Binding Free Energy Function in GLIDE
vdW desol elec const
vdW
desol
elec
const
ΔG=ΔE +ΔG +ΔE +ΔG
ΔE :
ΔG :
ΔE :
ΔG :
Van der Waals energy; i.e. Shape complementarity
Desolvation energy; i.e. Hydrophobicity
Electrostatic interaction energy and
Translational, rotational and vibrational free energy changes
desol
ΔG = N ΔG
N :
ΔG :
i i
i
i
i
Number of atoms of type i
Desolvation energy for an atom of type i
5. scoring of binding poses
22. The perfect scoring function will…
• Accurately calculate the binding affinity
– Will allow actives to be identified in a virtual screen
– Be able to rank actives in terms of affinity
• Score the poses of an active higher than poses of an inactive
– Will rank actives higher than inactive in a virtual screen
• Score the correct pose of the active higher than an incorrect pose of
the active
– Will allow the correct pose of the active to be identified
Where “actives” = molecules with biological activity
23. The Drug discovery schema today and Virtual
screening
Virtual Screening: Virtual screening is a collection of
tools to search libraries of small molecules in order to
identify those structures which are most likely to match to
a query (being a protein or a small molecule ligand).
26. Achievements of virtual screening in drug discovery
1 (IC50 = 27 μM)
Hit identified in virtual screening
from Merck Sample collection
L-700, 462 (IC50 = 0.011 μM)
Known as Aggraset lunched in 1998
Structure based
Lead optimization
1. AGGRASET
2. PRX00023
PRX-93009 (Initial virtual hit)
Ki (5HT1A = 1 nM)
Ki (α1 = 6 nM)
Ki (α2 = 12 nM)
PRX-00023 (Optimized lead) Phase IIb
Ki (5HT1A = 5 nM)
Ki (α1 = > 10 μM)
Ki (α2 = >10 μ M)
Structure based
Lead optimization
5HT1A receptor agonist used in major depression patients
Fibrinogen receptor antagonist used as platelet aggregation inhibitor in myocardial infraction
Expert. Opin. Drug Discov. 2008, 3, 841-851. WO2006/081332
27. Achievements of virtual screening in drug discovery
PRX-93046 (Ki = 21 nM)
PRX-03140 (Ki = 1 nM)
Phase IIb alone and in combination with Aricept
Structure not disclosed
3. PRX-03140
4. PRX-08066
PRX-08066 (optimized lead) Phase IIa
Ki (5HT2B = 3.4 nM)
Structure based
Lead optimization
5HT2B antagonist useful in pulmonary arterial hypertension associated with COPD
Partial agonist of 5HT4 useful in alzheimer disease
Structure based
Lead optimization
(Ki = 570 nM)
EPIX pharmaceuticals virtual hit
EPIX pharmaceuticals virtual hit
Expert. Opin. Drug Discov. 2008, 3, 841-851. WO2006/081332
28. Achievements of virtual screening in drug discovery
Expert. Opin. Drug Discov. 2008, 3, 841-851. WO2006/081332
Cyclic aliphatic bicarboxylic
acid derivative in which one
carboxylic acid is amide
bound to biphenyl group
SC12267 (IC50 = 134 nM)
Phase II studies
5. Vidofludimas
6. Mozenavir
DMP-450 or mozenavir (Ki = 0.28 nM)
Reached upto Phase IIb trial
Structure based
Lead optimization
Hit from ligand based virtual screening
Ki = > 4 μM concentration
EPIX pharmaceuticals virtual hit
Initial Lead (IC50 = 410 nM)
Structure based
Lead optimization
Dihydroorotate dehydrogenase (DHODH) inhibitor for immunomodulation in rhemutoid arthritis
Protease inhibitor for AIDS failed due to bioavaibility issues
29. Summary of presentation
• The last 40 years exhaustive research led to the development of Structure based
drug design ass powerful tool currently employed in drug discovery.
• Virtual screening is an combination of methods used to improve the enrichment
of active lead candidate from large databases.
• Flexible docking methods are mostly used due to superior enrichment results
over rigid docking.
• Docking scoring function prioritizes the set of compound based on the plausible
binding affinity. However, still the selection of compound for biological
validation is still a require a human command.
•
31. Combined ligand and structure based approaches for identification
and optimization of new CYP1B1 inhibitor leads
CYP1B1 is considered as one of the most appropriate molecular mechanism for
metabolism of cisplatin in cancer cells and therefore causes chemoresistance to cisplatin.
Recently Walsh et al elucidated the crystal structure of CYP1B1 with potent inhibitor α-
napthoflavone. Which could be utilized to search novel CYP1B1 inhibitor scaffolds.
O
O
Alpha-Napthoflavone
CYP1B1 IC50= 4 nM
Mol. Cell Biol. 1990, 10, 5098.
Cancer Res. 1993, 53, 2279. J. Biol Chem. 2013, 288, 12932-12941.
Literature reports suggest that no such protocol is available for identification of new chemotypes
for CYP1 family inhibition.
32. α-Napthoflavone
GLIDE Docking score = -11.07
MMGB/SA binding affinity ΔG = -82.82
Therefore 50,000 in-house molecules were subjected to the vHTS by molecular
docking and ΔG of binding calculation by prime MMGB/SA calculation using
depicted strategy.
Molecular docking and binding affinity calculations
HTVS docking
SP docking
XP docking
MMGB/SA
Top 30 %
Top 30 %
Top 30 %
Docking by Glide
MMGB/SA by Prime
CYP1B1 ANF-CYP1B1 complex
33. Site Score
R4 -1.83
R3 -1.52
R5 -1.43
R6 -1.26
ANF-CYP1B1
complex
ANF E-pharmacophore
E-Pharmacophore design and screening
Ligand based screening is one of the powerful tool to identify the new chemotypes
matching the query ligand.
From structure based docking, those sites which are crucial for target CYP1B1 binding
were recruited based on site score and incorporated to the E-pharmacophore
Therefore 50,000 in-house molecules were subjected to E-pharmacophore screening using PHASE.
34. 50,000 in house compounds
Best 1000
Compounds
Knowledge based selection of
300 candidates for screening
CYP1B1 structure
guided virtual
Screening
E-Pharmacophore
screening using
ANF O
O
ANF
CYP1A1 IC50= 60 nM
CYP1A2 IC50= 6 nM
CYP1B1 IC50= 4 nM
CYP1B1 Structure
Experimental Validation
of proposed hits
Combined Ligand and structure based virtual screening
HTVS docking
SP docking
XP docking
MMGB/SA
Top 30 %
Top 30 %
Top 30 %
36. 10 ns MD
Simulation of
5121780 (119)
with CYP1
enzymes
IC50 calculation of 5 compounds in
SacchrosomesTM and live human cells
Commercially purchased 50,000
compounds library
CYP1A1 Structure Based and ANF e-
Pharmacophore screening
CYP1A1 inhibition of 300 ligands in
EROD assay using sacchrosomesTM
300 hits
Virtual screening
5 compounds
N
N
5121780 (119)
Live human cells
CYP 1A1 IC50: 269 nM
CYP 1B1 IC50: 56 nM
CYP1A2 IC50: 30 nM
CYP2D6, 2C9, 2C19, 3A4 IC50: >10 M Potent
CYP1 inhibitor
Summary of research work