NEERJA SHUKLA (neerjashuklaau2007@rediffmail.com)

CHEMISTRY, UNIVERSITY OF ALLAHABAD

March, 2014

Abstract

The work embodied deals with the QSAR based modeling of Central Nervous System drugs. This is divided into five main topics: First one introduces the QSAR methodology A quantitative structure-activity relationship (QSAR) correlates measurable or calculable physical or molecular properties to some specific biological activity in terms of an equation. Once a valid QSAR has been determined, it should be possible to predict the biological activity of related drug candidates before they are put through expensive and time-consuming biological testing. In some cases, only computed values need to be known to make an assessment. Following QSAR approaches have been widely used by scientists are- i) Hansch analysis ii) Free Wilson Method iii) Cramer’s sub structural analysis iv) Principal component analysis v) Rank correlation analysis vi) Linear discriminate analysis 3D QSAR Comparative Molecular Field Analysis (CoMFA) – Regression Analysis Biological data In QSAR analysis, it is imperative that the biological data must be both, accurate and precise to develop a meaningful model. Biological data are usually expressed on logarithmic scale because of their linear relationship between response and log dose in the mid region of the log dose-response curve. Inverse logarithms for activity (log 1/c) are used so that higher values are obtained for more effective analysis. All diverse kind of biological data viz affinity data, like structure or receptor binding constant, rate constant and other in-vitro and in-vivo biological activity data like ki, IC50, ED50 and ID50 are used frequently. Correlation coefficient (R): Other Important Statistical Parameters Following are some of the important statistical parameters in addition to the above, which are also used to test the significance the regression equation. (1) Predictive Correlation Coefficient (R2pred) : (2) Goodness of Fit (SD) or standard Deviation SD = (3) Predictive Square Error (PSE): PSE = (4) Coefficient of Alienation (K): K = (5) Probable error of correlation coefficient (PE): PE = (6) Pogliani Quality Parameter: (7) Index of Forecasting Efficiency (E): E = 100 (8) Validation Methods (a) Cross - Validation Technique Evaluation of Predictive Power of QSAR Equations Outliers in QSAR Modeling Second one describes the role of QSAR in drug designing In the past 45 years, the use of QSAR, since the advent of this methodology has become increasingly helpful in understanding many aspects of chemical-biological interactions in drug and pesticide research, as well as in the field of toxicology. This method is useful in elucidating the mechanisms of chemical-biological interactions in various biomolecules, particularly enzymes as well as membranes, organelles and in humans. It has also been used for the evaluation of absorption, distribution, metabolism and excretion (ADME) phenomena in many organisms and whole animal studies. • Molecular modeling first introduced in the pharmaceutical industries in the early 70’s have raised probably unrealistic hopes. But it took quite a while before it could deliver. With the ever-expanding new powerful methods available, today’s modelers have the requisite potential to bring real benefits to pharmaceutical industry. • Molecular modeling and computational chemistry are essential to understand the molecular basis for biological activity and has tremendous potential to aid drug Designing. A healthy interaction between computational chemists and pharmaceutical industry seem indispensable. Structure Based Drug Design is an extremely important tool in the computer aided drug design. Following are some of the drugs derived from QSAR or structure-based approaches: Norfloxacin as an antibacterial agent, Metconazole useful as a fungicide, Captopril used in Hypertension, Dorzolamide used for Glaucoma, Nelfinavir used in HIV/ AIDS, Oseltamivir used for treatment of influenza and Imatinib useful for chronic myelogenous leukaemia. Quantitative structure-activity relationships (QSAR) offer the possibility for screening a large number of chemicals in a short time with low cost. QSAR have also been successfully applied to predict the soil sorption coefficients of non-polar and non-ionizable organic compounds including many pesticides. The role of lipophilicity as well as dissociation and ionization in drug absorption, transport and distribution could only be understood after correlating the biological activity data by appropriate QSAR models. Thus QSAR and 3D QSAR are the tools to derive hypothesis which should be proven or disproven by further syntheses and biological test. The predictions in QSAR are only a means for the design of new analogs. Third one deals with C.N.S. diseases and drugs The nervous system is a complex, sophisticated system that regulates and coordinates the basic functions and activities of the body. It is made up of: Central nervous system consisting of the brain and spinal cord and the peripheral nervous system consisting of all neural elements. The brain is lodged in the skull while the spinal cord is enclosed by the vertebral column. This thesis deals with following five diseases of central nervous system-Parkinson’s disease, Alzheimer’s disease, Multiple sclerosis, Anxiety and Schizophrenia. 1. Parkinson’s disease Progressive neurological disorder of muscle movement leads to Parkinson’s disease. It generally starts in middle or late life, affecting 1-2 per 1,000 of the general population and upto 2 per 100 people over 65 years. Symptoms of Parkinson’s disease include: Tremor, Muscular rigidity, Bradykinesia and Postural abnormalities. Medications for Parkinson’s disease The following medications may be prescribed for the treatment of Parkinson’s disease: (i) Bromocriptine-BC (ii) Cabaser (iii) L-DOPA (iv) Amantadine 2. Alzheimer’s disease Alzheimer’s disease (AD) also called senile dementia of the Alzheimer type or simply Alzheimer’s is the most common form of dementia. It is a neurodegenerative disease typically found in people over the age of 65 years. Symptoms of Alzheimer’s disease In the early stages of Alzheimer’s disease, the most common symptom is the inability to acquire new memories or difficulty in recalling recently observed events. In the advanced stage of the disease, symptoms include confusion, irritability and aggression, mood swings, language breakdown, long term memory loss and the general withdrawal of the sufferer as their senses decline. Gradually body functions are lost, ultimately leading to death. Medications for Alzheimer’s disease The medications used to treat Alzheimer’s disease include: (i) Razadyne (ii) Exelon (iii) Aricept (iv) Cognex (v) Namenda 3. Multiple Sclerosis Multiple Sclerosis (MS) is also known as disseminated sclerosis or encephalomyelitis disseminata. Symptoms of Multiple sclerosis A person suffering with Multiple sclerosis (MS) can show neurological symptom or sign including changes in sensation such as loss of sensitivity or tingling, pricking or numbness (hypoesthesia and paraesthesia), muscle weakness, clonus, muscle spasms or difficulty in moving, difficulties with coordination and balance(ataxia), problems in speech (dysarthria) or swallowing (dysphagia),visual problems, fatigue. Emotional symptoms of depression or unstable mood are also common. Medications for Multiple Sclerosis (MS) Following medications may be used for the treatment MS. (i) Interferons (a) Interferon beta-1a (b) Interferon beta-1b (ii) Glatiramer acetate (iii) Natalizumab 4. Anxiety Anxiety is a psychological and physiological state characterized by somatic, emotional, cognitive and behavioral components. Symptoms of anxiety Following are the signs and symptoms of anxiety disorders: (a) Emotional symptoms of anxiety This includes feelings of dread, irritability, restlessness, watching for signs of danger, feeling tense and feeling like the mind has gone blank. (b) Physical symptoms of anxiety Common physical symptoms of anxiety include sweating, dizziness, frequent urination or diarrhea, shortness of breath, muscle tension, headaches, fatigue and insomnia. Medications for Anxiety For treatment of anxiety disorders following medications are used: (i) Alprazolam (ii) Clonazepam (iii) Sertraline (iv) Escitalopram (vi) citalopram 5. Schizophrenia Schizophrenia is a mental disorder characterized by disintegration of thought processes and emotional responsiveness. It is also sometimes called split personality disorder Symptoms of Schizophrenia Symptoms of schizophrenia may be divided into three categories- • Positive symptoms of schizophrenia includes delusions, hallucinations, disorganized speech/thinking/behavior. • Negative symptoms include lack of emotions, low memory, lack of interest in life, low motivation, alogia(difficulty or inability to speak) and social isolation. • Cognitive symptoms of schizophrenia refer to the difficulties in concentration and includes disorganized thinking, slow thinking, difficulty in understanding, poor memory. Medications for Schizophrenia The medications used for the treatment of schizophrenia can be divided into following two categories: (a) Typical antipsychotic drugs (1st generation) (i) Thioridazine (Melleril) (ii) Fluphenazine (Prolixin) (iii) Molindone (Moban) (iv) Chlorpromazin (Thorazine) (b) Atypical antipsychotic drugs (2nd generation) (i)Aripiprazole (Abilify) (ii) Olanzapine (Zyprexa) (iii) Paliperidone (Invega) (iv) Quetiapine (Seroquel) Fourth one includes parameters used for present thesis Following parameters have been used in the present thesis: Partition coefficient (Log P) Molecular Weight (Mw) Molar Volume (MV) Molar Refractivity (MR) Parachor (Pc) Density (D) Index of refraction (IOR) Surface Tension (ST) Equalized Electro-negativity (Xeq) Polarizibility (Pz) Weiner Index (W) Balaban distance connectivity index (J) Balaban centric Index (BAC) Molecular connectivity (χ) Zero-order connectivity index [°χ] First order connectivity (1χ) The first order connectivity index is represented by following expression. 1χ(G) = 1χ = where ‘s’ stands for an edge in G while Se is the total number of edges in G each edge of G has in this case a weight of DiDj. The first order connectivity index is ofcourse, identical to the original Randic’s connectivity index. Second order connectivity index (2χ) Indicator variables Fifth is the last main chapter of this thesis which describes results and discussion which contains: 1 QSAR Studies on a series of carbamate appended N-alkylsulfonamides as inhibitors of peptide amyloid- β (Aβ) 2 QSAR studies on a series of off-target ion channel selective diltiazem sodium derivatives 3 QSAR Studies on a series of 9-tetrahydrocannabinol ( 9-THC) analogues as cannabinoid receptor modulators 4 QSAR Based Modeling on a series of lactam fused chroman derivatives as selective 5-HT transporter 5 QSAR Studies on a series of 1, 3-dioxolane based 1-adrenoreceptor antagonists 6 QSAR based modeling on a series of α-Hydroxy amides as a novel class of bradykinin B1 selective antagonists 7 QSAR Based modeling on a series of 2-chloro-N6-substituted-4′-thioadenosine-5′-N, N-dialkyluronamides as human A3 adenosine receptor antagonists 8 QSAR analysis of cyclohexanamine class of human serotonin transporter (hSERT) inhibitory activities 9 QSAR studies on a series of imidazole derivatives as novel ORL1 receptor antagonists References: 1) QSAR Based Modeling on a series of alpha-Hydroxy amides as a novel class of Braykinin B1 selective antagonists. A.K. Srivastava, N. Shukla, A. Pandey and A. Srivastava, J. Saudi Chem. Soc., 15, 2011, 215-220. 2) QSAR based modeling of hepatitis C virus NS5B inhibitors .A.K. Srivastava , A. Pandey, A. Srivastava and N. Shukla, J. Saudi Chem. Soc., 15, 2011, 25-28. 3) Quantitative Structure Activity Relationship (QSAR) Studies on a series of off-target ion channel selective diltiazem sodium derivatives. A. K. Srivastava, N. Shukla and V. K. Pathak, J. Indian Chem. Soc., 87, 2010, 1-7. 4) QSAR based modeling on a novel series of pyrimidine-4-carboxamides as antagonists of the human A1 receptor. A.K. Srivastava, N. Shukla, A. Pandey, Oxidation Communication, 2, 2012, 414-422. 5) QSAR studies on a novel cyclohexanamine class of human serotonin transporter (hSERT) inhibitory activities. N. Shukla, A.K. Srivastava, Oxidation Communication, 1, 2013, 134-142. 6) QSAR study on TIE-2 inhibitors: Dominating role of Topological parameters. A.K.Srivastava, Akanchha Srivastava, N. Shukla, Oxidation Communication, 1, 2013, 143-155. 7) Quantitative Structure ActivityRelationship(QSAR) Studies on a series of carbamate appended N-alkylsulfonamides as inhibitors of peptide amyloid- β (Aβ). A. K. Srivastava, N. Shukla and V. K. Pathak Oxidation Communication, 4, 2013, 1080-1089 8) Quantitative Structure Activity Relationship (QSAR) Studies on a series of imidazole derivatives as novel ORL1 receptor antagonists A.K. Srivastava, N. Shukla, J. Saudi Chem. Soc., 17, 2013, 321-328. 9) QSAR based modeling on a series of lactam fused chroman derivatives as selective 5-HT transporters, A. K. Srivastava, N. Shukla, J. Saudi Chem. Soc., 16, 2012, 405-412. 10. QSAR studies on a series of 9-THC analogues as cannabinoid receptor modulators, N. Shukla, IJRSTM, 2014 11. In-silico analysis of different plant protein and their essential compound with sulfonylurea binding protein of beta-cells of homo sapiens for curing diabetes mellituss type II, Eur. Chem. Bull., R. Sahu and N. Shukla, 2014, 3, 568-576.