Computational Pharmaceutics

Computational Pharmaceutics


While computer‐aided drug design (or rational drug design) has been practised for half a century, the application of computational modeling to drug delivery and pharmaceutical formulations “computational pharmaceutics” has emerged only in recent years. In combination with existing branches of pharmaceutics, it offers rapidly growing potential for developing rational, deductive, and knowledge‐based strategies in pharmaceutics. Thus, this discipline has emerged and grown enormously in importance. Exploiting the exponential growth in power of high performance computing systems, computational  pharmaceutics has the ability to provide multiscale lenses to pharmaceutical scientists, revealing mechanistic details ranging across chemical reactions, small drug molecules, proteins, nucleic acids, nanoparticles, and powders to the human body.
Given the advances in the field, it is becoming increasingly important that pharmaceu-tical scientists have cognizance of computational modeling, which may be anticipated to become a more common part of postgraduate curricula. While the fundamental theories and technical implementations of many of the methods in this book are complex, the  computational software is becoming much more readily accessible and usable. Hence, it is no longer a prerequisite to understand in detail how the methods work at the theory and computer coding levels in order to gain substantial insights and benefits from carrying out simulations. Written for an audience with little experience in molecular modeling, with a focus on applications, this book will prove an excellent resource for researchers and  students working in pharmaceutical sciences. We anticipate it will be useful not only for pharmaceutical scientists but more broadly for computational chemists looking to move into the pharmaceutical domain, for those working in medicinal chemistry, materials  science, and nanotechnology.

Computational Pharmaceutics

It is a given that active pharmaceutical ingredients (APIs) should be made into safe and effective dosage forms or formulations before administration to patients. Pharmaceutics is the discipline to make an API into the proper dosage form or medicine, which may then be safely and effectively used by patients [1]. Pharmaceutics also relates to the absorption, distribution, metabolism, and excretion of medicines in the body. Branches of pharmaceutics include formulation development, pharmaceutical manufacture and associated technologies, dispersing pharmacy, physical pharmacy, pharmacokinetics, and biopharmaceutics [1–4]. Today there are various dosage forms, such as tablets, capsules, solutions, suspensions, creams, inhalations, patches, and recently nanomedicines (e.g., liposomes, nanoparticles, nanopatches). Although numerous new techniques have been developed for the form of dosage, current development of drug formulations still strongly relies on personal experience of pharmaceutical scientists by trial and error in the laboratory [1]. The process of formulation development is laborious, time‐ consuming, and costly. Therefore, the simplification of formulation development becomes more and more important in pharmaceutical research. Computational pharmaceutics involves the application of computational modeling to drug delivery and pharmaceutical 2 Computational Pharmaceutics
nanotechnology. In combination with existing branches of pharmaceutics, it offers  rapidly growing potential for developing rational, deductive and knowledge‐based strategies in pharmaceutics.
With stunningly rapid advances in hardware, theory and software algorithms, com-puter simulation is now able to model complex systems, which may be difficult, costly, or even impractical to measure or monitor directly by experiment [5, 6]. The first example of computer modeling was the simulation of the nuclear bomb process in the Manhattan Project, World War II. With the development of high performance computing, multiscale modeling techniques have been widely pursued, from quantum mechanics (QM) and molecular dynamics (MDs) to stochastic Monte Carlo methods, coarse grained dynamics, discrete element methods (DEMs), finite element methods as well as advanced analytical modeling. In principle, all properties of all systems are able to be described by QM. However, first principle calculations are limited to small systems, <1000 atoms, which is impractical for solving applications of large mole-cules or systems [5, 6]. MD simulations mimic the physical motion of atoms and mol-ecules under Newton’s laws of physics, which is applicable to larger systems containing millions of atoms [5, 6]. MD simulation is based on molecular mechanics, which models the interactions between atoms with force fields. Monte Carlo (MC) simulation uses the same empirical force field as MD simulation. However, MC sim-ulation features playing and recording the results in casino‐like conditions by repeated random sampling [5, 6]. Thus, unlike MD simulation, MC simulation cannot offer dynamical information with time evolution of the system in a form suitable for view-ing. MC methods are especially useful for modeling systems with significant uncer-tainty and high degrees of freedom, such as polymer chains and protein membranes. For much larger systems, coarse‐grain models do further classical approximations by  treating functional groups as rigid bodies of constrained particles [5,  6]. The DEM is one of the numerical methods for computing the motion and effect of a large number of small particles, which is widely used in the pharmaceutical process and manufacturing [5, 6].
In the past three decades, the application of computational modeling approaches in the field of drug design (e.g., QSAR, ligand docking) has been intensively developed to the point of being a mature field [7]. Pharmaceutical research is, however, a far broader field than drug design alone. Proceeding beyond drug design, the application of computational modeling to drug delivery and pharmaceutical nanotechnology, com-putational pharmaceutics, is a very new field with great potential for growth [8]. As shown in Figure 1.1, computational pharmaceutics has the ability to provide multiscale lenses to pharmaceutical scientists, revealing mechanistic details ranging across the chemical reactions of small drug molecules, proteins, nucleic acids, nanoparticles, and powders with the human body. The aim of this book is to provide a contemporary over-view of the application of computational modeling techniques to problems relating to pharmaceutics (drug delivery and formulation development) that will be of great rele-vance for pharmaceutical scientists and computational chemists in both industry and academia. Contributions from leading researchers cover both computational modeling methodologies and various examples where these methods have been applied successfully in this field.
   
  


For Download Click on the following:

                                                     download


No comments:

Post a Comment