Current Topics in Medicinal Chemistry - Volume 1, Issue 4, 2001

Optimizing chemical structures to create potentially safe drugs during discovery and early development relies on a combination of predictive algorithms, screening, formal toxicology studies, and early clinical trials. Early in the process three critical questions emerge that must be answered by a detailed profiling approach. These questions are: 1) is there a correlation between the chemical structure and potential toxicity that can be used to optimize structures of lead compounds, 2) can specific markers of potential toxicity can be identified early and used as mechanistic decision-making screens, and 3) will exposures (plasma levels) in animal studies correlate with exposures encountered in the clinic thereby providing coverage for safety? Depending on the therapeutic class of compounds being considered and the level of knowledge available, feedback loops of information can be established to guide the development process.

Computational toxicity modeling can have significant impact in the drug discovery process, especially when utilized as a sentinel filter for common drug safety liabilities, such as mutagenicity, carcinogenicity and teratogenicity. This review will focus on the strengths and limitations of the current computational models for predicting these drug safety liabilities, and the various strategies for incorporating these predictive models into the drug discovery process.

A fundamental fact in the drug development process is that physical quantity of chemical substance is needed for experimental determinations. Information that could guide the course of a drug discovery program, in particular the penultimate ADME parameters percent oral bioavailability (percentF) and CNS permeability (BBB) are not explicitly determined until after large capital, human and time resources have been invested in a particular chemical series to produce the substance. To assure better go / no-go decisions and to protect the risks of a process that necessitates a considerable front-loading of resources, project teams are turning to computational methods to estimate these parameters. Herein we provide a detailed review of holistic in silico methods toward the estimation of percentF and BBB. An unbiased description of the scope and limitations of their installation and application will be given in the context of an on going pharmaceutical project.

About 30per4cent of drug candidate molecules are rejected due to pharmacokinetic-related failures. When poor pharmaceutical properties are discovered in development, the costs of bringing a potent but poorly absorbable molecule to a product stage by formulation can become very high. Fast and reliable in vitro prediction strategies are needed to filter out problematic molecules at the earliest stages of discovery. This review will consider recent developments in physicochemical profiling used to identify candidate molecules with physical properties related to good oral absorption. Poor solubility and poor permeability account for many PK failures. FDAs Biopharmaceutics Classification System (BCS) is an attempt to rationalize the critical components related to oral absorption. The core idea in the BCS is an in vitro transport model, centrally embracing permeability and solubility, with qualifications related to pH and dissolution. The objective of the BCS is to predict in vivo performance of drug products from in vitro measurements of permeability and solubility. In principle, the framework of the BCS could serve the interests of the earliest stages of discovery research. The BCS can be rationalized by considering Ficks first law, applied to membranes. When molecules are introduced on one side of a lipid membrane barrier (e.g., epithelial cell wall) and no such molecules are on the other side, passive diffusion will drive the molecules across the membrane. When certain simplifying assumptions are made, the flux equation in Ficks law reduces simply to a product of permeability and solubility. Many other measurable properties are closely related to permeability and solubility. Permeability (Pe) is a kinetic parameter related to lipophilicity (as indicated by the partition and distribution coefficients, log P and log D). Retention (R) of lipophilic molecules by the membrane (which is related to lipophilicity and may predict PK volumes of distribution) influences the characterization of permeability. Furthermore, strong drug interactions with serum proteins can influence permeability. The unstirred water layer on both sides of the membrane barrier can impose limits on permeability. Solubility (S) is a thermodynamic parameter, and is closely related to dissolution, a kinetic parameter. The unstirred water layer on the surfaces of suspended solids imposes limits on dissolution. Bile acids effect both solubility and dissolution, by a micellization effect. For ionizable molecules, pH plays a crucial role. The charge state that a molecule exhibits at a particular pH is characterized by the ionization constant (pKa) of the molecule. Buffers effect pH gradients in the unstirred water layers, which can dramatically affect both permeability and dissolution of ionizable molecules. In this review, we will focus on the emerging instrumental methods for the measurement of the physicochemical parameters Pe, S, pKa, R, log P, and log D (and their pH-profiles). These physicochemical profiles can be valuable tools for the medicinal chemists, aiding in the prediction of in vivo oral absorption.