Current Pharmaceutical Design - Volume 18, Issue 9, 2012

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting proteinligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/ optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches.

Novel discoveries in molecular disease pathways within the cell, combined with increasing information regarding protein binding partners has lead to a new approach in drug discovery. There is interest in designing drugs to modulate protein-protein interactions as opposed to solely targeting the catalytic active site within a single enzyme or protein. There are many challenges in this new approach to drug discovery, particularly since the protein-protein interface has a larger surface area, can comprise a discontinuous epitope, and is more amorphous and less well defined than the typical drug design target, a small contained enzyme-binding pocket. Computational methods to predict modes of protein-protein interaction, as well as protein interface hot spots, have garnered significant interest, in order to facilitate the development of drugs to successfully disrupt and inhibit protein-protein interactions. This review summarizes some current methods available for computational protein-protein docking, as well as tabulating some examples of the successful design of antagonists and small molecule inhibitors for protein-protein interactions. Several of these drugs are now beginning to appear in the clinic.

Novel discoveries in molecular disease pathways within the cell, combined with increasing information regarding protein binding partners has lead to a new approach in drug discovery. There is interest in designing drugs to modulate protein-protein interactions as opposed to solely targeting the catalytic active site within a single enzyme or protein. There are many challenges in this new approach to drug discovery, particularly since the protein-protein interface has a larger surface area, can comprise a discontinuous epitope, and is more amorphous and less well defined than the typical drug design target, a small contained enzyme-binding pocket. Computational methods to predict modes of protein-protein interaction, as well as protein interface hot spots, have garnered significant interest, in order to facilitate the development of drugs to successfully disrupt and inhibit protein-protein interactions. This review summarizes some current methods available for computational protein-protein docking, as well as tabulating some examples of the successful design of antagonists and small molecule inhibitors for protein-protein interactions. Several of these drugs are now beginning to appear in the clinic.

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of proteinprotein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

Most biological processes involve multiple proteins interacting with each other. It has been recently discovered that certain residues in these protein-protein interactions, which are called hot spots, contribute more significantly to binding affinity than others. Hot spot residues have unique and diverse energetic properties that make them challenging yet important targets in the modulation of proteinprotein complexes. Design of therapeutic agents that interact with hot spot residues has proven to be a valid methodology in disrupting unwanted protein-protein interactions. Using biological methods to determine which residues are hot spots can be costly and time consuming. Recent advances in computational approaches to predict hot spots have incorporated a myriad of features, and have shown increasing predictive successes. Here we review the state of knowledge around protein-protein interactions, hot spots, and give an overview of multiple in silico prediction techniques of hot spot residues.

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. In this review we discuss the challenges involved in both the building of in silico models on toxicology endpoints and their practical use in decision making. In particular, we will reflect upon the predictive strength of a number of different in silico models for a range of different endpoints, different approaches used to generate the models or rules, and limitations of the methods and the data used in model generation. Given that there exists no unique definition of a ‘good’ model, we will furthermore highlight the need to balance model complexity/interpretability with predictability, particularly in light of OECD/REACH guidelines. Special emphasis is put on the data and methods used to generate the in silico toxicology models, and their strengths and weaknesses are discussed. Switching to the applied side, we next review a number of toxicity endpoints, discussing the methods available to predict them and their general level of predictability (which very much depends on the endpoint considered). We conclude that, while in silico toxicology is a valuable tool to drug discovery scientists, much still needs to be done to, firstly, understand more completely the biological mechanisms for toxicity and, secondly, to generate more rapid in vitro models to screen compounds. With this biological understanding, and additional data available, our ability to generate more predictive in silico models should significantly improve in the future.

The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. In this review we discuss the challenges involved in both the building of in silico models on toxicology endpoints and their practical use in decision making. In particular, we will reflect upon the predictive strength of a number of different in silico models for a range of different endpoints, different approaches used to generate the models or rules, and limitations of the methods and the data used in model generation. Given that there exists no unique definition of a ‘good’ model, we will furthermore highlight the need to balance model complexity/interpretability with predictability, particularly in light of OECD/REACH guidelines. Special emphasis is put on the data and methods used to generate the in silico toxicology models, and their strengths and weaknesses are discussed. Switching to the applied side, we next review a number of toxicity endpoints, discussing the methods available to predict them and their general level of predictability (which very much depends on the endpoint considered). We conclude that, while in silico toxicology is a valuable tool to drug discovery scientists, much still needs to be done to, firstly, understand more completely the biological mechanisms for toxicity and, secondly, to generate more rapid in vitro models to screen compounds. With this biological understanding, and additional data available, our ability to generate more predictive in silico models should significantly improve in the future.

A successful, efficacious and safe drug must have a balance of properties, including potency against its intended target, appropriate absorption, distribution, metabolism, and elimination (ADME) properties and an acceptable safety profile. Achieving this balance of, often conflicting, requirements is a major challenge in drug discovery. Approaches to simultaneously optimizing many factors in a design are broadly described under the term ‘multi-parameter optimization’ (MPO). In this review, we will describe how MPO can be applied to efficiently design and select high quality compounds and describe the range of methods that have been employed in drug discovery, including; simple ‘rules of thumb’ such as Lipinski's rule; desirability functions; Pareto optimization; and probabilistic approaches that take into consideration the uncertainty in all drug discovery data due to predictive error and experimental variability. We will explore how these methods have been applied to predicted and experimental data to reduce attrition and improve the productivity of the drug discovery process.

A successful, efficacious and safe drug must have a balance of properties, including potency against its intended target, appropriate absorption, distribution, metabolism, and elimination (ADME) properties and an acceptable safety profile. Achieving this balance of, often conflicting, requirements is a major challenge in drug discovery. Approaches to simultaneously optimizing many factors in a design are broadly described under the term ‘multi-parameter optimization’ (MPO). In this review, we will describe how MPO can be applied to efficiently design and select high quality compounds and describe the range of methods that have been employed in drug discovery, including; simple ‘rules of thumb’ such as Lipinski's rule; desirability functions; Pareto optimization; and probabilistic approaches that take into consideration the uncertainty in all drug discovery data due to predictive error and experimental variability. We will explore how these methods have been applied to predicted and experimental data to reduce attrition and improve the productivity of the drug discovery process.

Currently, there are two types of drugs on the market: orthosteric, which bind at the active site; and allosteric, which bind elsewhere on the protein surface, and allosterically change the conformation of the protein binding site. In this perspective we argue that the different mechanisms through which the two drug types affect protein activity and their potential pitfalls call for different considerations in drug design. The key problem facing orthosteric drugs is side effects which can occur by drug binding to homologous proteins sharing a similar binding site. Hence, orthosteric drugs should have very high affinity to the target; this would allow a low dosage to selectively achieve the goal of target-only binding. By contrast, allosteric drugs work by shifting the free energy landscape. Their binding to the protein surface perturbs the protein surface atoms, and the perturbation propagates like waves, finally reaching the binding site. Effective drugs should have atoms in good contact with the ‘right’ protein atoms; that is, the contacts should elicit propagation waves optimally reaching the protein binding site target. While affinity is important, the design should consider the protein conformational ensemble and the preferred propagation states. We provide examples from functional in vivo scenarios for both types of cases, and suggest how high potency can be achieved in allosteric drug development.

Currently, there are two types of drugs on the market: orthosteric, which bind at the active site; and allosteric, which bind elsewhere on the protein surface, and allosterically change the conformation of the protein binding site. In this perspective we argue that the different mechanisms through which the two drug types affect protein activity and their potential pitfalls call for different considerations in drug design. The key problem facing orthosteric drugs is side effects which can occur by drug binding to homologous proteins sharing a similar binding site. Hence, orthosteric drugs should have very high affinity to the target; this would allow a low dosage to selectively achieve the goal of target-only binding. By contrast, allosteric drugs work by shifting the free energy landscape. Their binding to the protein surface perturbs the protein surface atoms, and the perturbation propagates like waves, finally reaching the binding site. Effective drugs should have atoms in good contact with the ‘right’ protein atoms; that is, the contacts should elicit propagation waves optimally reaching the protein binding site target. While affinity is important, the design should consider the protein conformational ensemble and the preferred propagation states. We provide examples from functional in vivo scenarios for both types of cases, and suggest how high potency can be achieved in allosteric drug development.