Chemistry Reference
In-Depth Information
CHAPTER 3
ADME/Tox Predictions in Drug Design
Ricardo Pereira Rodrigues
1
, Jonathan Resende de Almeida
1
, Evandro Pizeta
Semighini
2
, Flávio Roberto Pinsetta
1
, Susimaire Pedersoli Mantoani
1
,
Vinicius Barreto da Silva
3
, Carlos Henrique Tomich de Paula da Silva
1
1
School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo,
Ribeirão Preto, SP, Brazil;
2
Ribeirão Preto Medical School, University of São
Paulo, Ribeirão Preto, SP, Brazil and
3
Catholic University of Goiás, Goiânia,
Brazil
Abstract
:
Most drug candidate failures during clinical trials occur due to
inappropriate ADMET properties. In this way, there is a major concern to identify
possible ADMET failures during the early stages of drug design projects and
optimize such properties in order to reduce time and costs.
In silico
ADMET
predictions comprise various strategies that play a central role when considering the
task of profiling lead compounds regarding potential ADMET failures. We will
discuss the computational strategies, methods and softwares used, actually, to profile
ADMET and how they could be helpful during drug design.
Keywords:
Absorption, ADME properties, bioavailability, distribution, drug
design, excretion, hydrogen bond acceptors, hydrogen bond donors,
in silico
predictions, Ionization constant, lipophilicity, LogP, metabolism, rule of five,
software, solubility, toxicity.
INTRODUCTION
The constant evolution of organic chemistry has accelerated the process of lead
discovery, contributing to the development of new drugs. However, this process is
still long and expensive, with growing costs as the process advances through
clinical trials [1]. Nevertheless, the majority of promising leads are eliminated due
to unfavorable ADME/Tox (Absorption, Distribution, Metabolism, Excretion and
Toxicity) properties, which stimulates the evaluation of these properties during
