QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS IN COMPUTER AIDED MOLECULAR DESIGN

2016 ◽  
Vol 78 (9-3) ◽  
Author(s):  
Hentabli Hamza ◽  
Naomie Salim ◽  
Faisal Saeed
Keyword(s):  
Drug Development ◽  
Development Process ◽  
Molecular Design ◽  
Therapeutic Effects ◽  
Drug Development Process ◽  
Quantitative Structure ◽  
Complete Evaluation ◽  
Chemical Examination ◽  
Structure Activity ◽  
Computer Aided

The drug development process requires the complete evaluation and identification of the chosen substance as well as its properties. It involves extensive chemical examination to achieve the best therapeutic effects which demands huge expenditure both in terms of time and money. Computer aided molecular design (CAMD) allows the production of new substances with pre-decided properties. Additionally, in order to illustrate and determine the interrelationship between the chemical structure of a compound and its biological activity, Quantitative Structure Activity Relationship (QSAR) is applied by employing a mathematical model and arranging molecular descriptors. This paper presents review of CAMD and QSAR techniques. The most common chemometric techniques are also emphasized. CAMD and QSAR are considered to be extremely efficient instruments in molecular design and accelerate the initial steps of drug development process. Furthermore, they enhance the effectiveness and reduce the cost of newly developed drugs.  

scholarly journals Quantitative Structure – Pharmacokinetic Relationships for Plasma Clearance of Basic Drugs with Consideration of the Major Elimination Pathway

2017 ◽  
Vol 20 ◽  
pp. 135 ◽  
Author(s):  
Zvetanka Dobreva Zhivkova
Keyword(s):  
Drug Development ◽  
Plasma Clearance ◽  
Development Process ◽  
External Validation ◽  
Structural Features ◽  
Drug Development Process ◽  
Basic Drugs ◽  
Quantitative Structure ◽  
Aromatic Rings ◽  
Elimination Pathway

Purpose. The success of a new drug candidate is determined not only by its efficacy and safety, but also by proper pharmacokinetic behavior. The early prediction of pharmacokinetic parameters could save time and resources and accelerate drug development process. Plasma clearance (CL) is one of the key determinants of drug dosing regimen. The aim of the study is development of quantitative structure – pharmacokinetics relationships (QSPkRs) for the CL. Methods. A dataset consisted of 263 basic drugs, which chemical structures were described by 154 descriptors.  Genetic algorithm, stepwise regression and multiple linear regression were used for variable selection and model development. Predictive ability of the models was assessed by internal and external validation.  Results. A number of significant QSPkR models for the CL were derived with respect to the primary elimination pathway (renal excretion, metabolism, or CYP3A4 mediated biotransformation), as well for the unbound clearance (CLu). The models were able to predict 52 – 80% of the drugs from external validation sets within the 2-fold error of the experimental values with geometric mean fold error 1.57 – 2.00. Conclusions. Plasma protein binding was the major restrictive factor for the CL of drugs, primarily cleared by metabolism.  The clearance was favored by lipophilicity and several structural features like OH-groups, aromatic rings, large hydrophobic centers, aliphatic groups, connected with electro-negative atoms, and non-substituted aromatic C-atoms. The presence of Cl-atoms and abundance of 6-member aromatic rings or fused rings had negative effect.  The presence of ether O-atoms contributed negatively to the CL of both metabolism and renally excreted drugs, and urine excretion was favored by the presence of 3-valence N-atoms. These findings give insight on the main structural features governing plasma CL of basic drugs and could serve as a guide for lead optimization in the drug development process. This article is open to POST-PUBLICATION REVIEW. Registered readers (see “For Readers”) may comment by clicking on ABSTRACT on the issue’s contents page.

Intelligent Drug Development

2014 ◽  
Author(s):  
Michael Tansey
Keyword(s):  
Clinical Trials ◽  
Cost Effectiveness ◽  
Drug Development ◽  
Clinical Research ◽  
Development Process ◽  
Drug Development Process ◽  
Individual Trial ◽  
Specific Technology

Clinical research is heavily regulated and involves coordination of numerous pharmaceutical-related disciplines. Each individual trial involves contractual, regulatory, and ethics approval at each site and in each country. Clinical trials have become so complex and government requirements so stringent that researchers often approach trials too cautiously, convinced that the process is bound to be insurmountably complicated and riddled with roadblocks. A step back is needed, an objective examination of the drug development process as a whole, and recommendations made for streamlining the process at all stages. With Intelligent Drug Development, Michael Tansey systematically addresses the key elements that affect the quality, timeliness, and cost-effectiveness of the drug-development process, and identifies steps that can be adjusted and made more efficient. Tansey uses his own experiences conducting clinical trials to create a guide that provides flexible, adaptable ways of implementing the necessary processes of development. Moreover, the processes described in the book are not dependent either on a particular company structure or on any specific technology; thus, Tansey's approach can be implemented at any company, regardless of size. The book includes specific examples that illustrate some of the ways in which the principles can be applied, as well as suggestions for providing a better context in which the changes can be implemented. The protocols for drug development and clinical research have grown increasingly complex in recent years, making Intelligent Drug Development a needed examination of the pharmaceutical process.

scholarly journals Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process

2015 ◽  
Vol 35 (7) ◽  
pp. 1063-1089 ◽  
Author(s):  
Sylwia Bujkiewicz ◽  
John R. Thompson ◽  
Richard D. Riley ◽  
Keith R. Abrams
Keyword(s):  
Drug Development ◽  
Analytical Methods ◽  
Development Process ◽  
Surrogate Endpoints ◽  
Drug Development Process

scholarly journals Precision Medicine: From “Omics” to Economics towards Data-Driven Healthcare - Time for European Transformation

2017 ◽  
Vol 2 (Suppl. 1) ◽  
pp. 1-10 ◽  
Author(s):  
Denis Lacombe ◽  
Lifang Liu ◽  
Françoise Meunier ◽  
Vassilis Golfinopoulos
Keyword(s):  
Drug Development ◽  
Development Process ◽  
Precision Oncology ◽  
Drug Development Process ◽  
Cancer Treatments ◽  
True Value ◽  
Efficient Access ◽  
Funding Agencies ◽  
Anti Cancer

There is room for improvement for optimally bringing the latest science to the patient while taking into account patient priorities such as quality of life. Too often, regulatory agencies, governments, and funding agencies do not stimulate the integration of research into care and vice versa. Re-engineering the drug development process is a priority, and healthcare systems are long due for transformation. On one hand, patients need efficient access to treatments, but despite precision oncology approaches, efficiently shared screening platforms for sorting patients based on the biology of their tumour for trial access are lacking and, on the other hand, the true value of cancer care is poorly addressed as central questions such as dose, scheduling, duration, and combination are not or sub-optimally addressed by registration trials. Solid evidence on those parameters could potentially lead to a rational and wiser use of anti-cancer treatments. Together, optimally targeting patient population and robust comparative effectiveness data could lead to more affordable and economically sound approaches. The drug development process and healthcare models need to be interconnected through redesigned systems taking into account the full math from drug development into affordable care.

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