The influence of lipophilicity in drug discovery and design

Introduction: The role of lipophilicity in drug discovery and design is a critical one. Lipophilicity is a key physicochemical property that plays a crucial role in determining ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties and the overall suitability of drug candidates. There is increasing evidence to suggest that control of physicochemical properties such as lipophilicity, within a defined optimal range, can improve compound quality and the likelihood of therapeutic success.

Areas covered: This review focuses on understanding lipophilicity, techniques used to measure lipophilicity, and summarizes the importance of lipophilicity in drug discovery and development, including a discussion of its impact on individual ADMET parameters as well as its overall influence on the drug discovery and design process, specifically within the past 15 years.

Expert opinion: A current review of the literature reveals a continued reliance on the synthesis of novel structures with increased potency, rather than a focus on maintaining optimal physicochemical properties associated with ADMET throughout drug optimization. Particular attention to the optimum region of lipophilicity, as well as monitoring of lipophilic efficiency indices, may contribute significantly to the overall quality of candidate drugs at different stages of discovery.     

Lipophilicity and biomimetic properties to support drug discovery

Introduction: Lipophilicity, expressed as the octanol-water partition coefficient, constitutes the most important property in drug action, influencing both pharmacokinetic and pharmacodynamics processes as well as drug toxicity. On the other hand, biomimetic properties defined as the retention outcome on HPLC columns containing a biological relevant agent, provide a considerable advance for rapid experimental – based estimation of ADME properties in early drug discovery stages.

Areas covered: This review highlights the paramount importance of lipophilicity in almost all aspects of drug action and safety. It outlines problems brought about by high lipophilicity and provides an overview of the drug-like metrics which incorporate lower limits or ranges of logP. The fundamental factors governing lipophilicity are compared to those involved in phospholipophilicity, assessed by Immobilized Artificial Membrane Chromatography (IAM). Finally, the contribution of biomimetic properties to assess plasma protein binding is evaluated.

Expert opinion: Lipophilicity and biomimetic properties have important distinct and overlapping roles in supporting the drug discovery process. Lipophilicity is unique in early drug design for library screening and for the identification of the most promising compounds to start with, while biomimetic properties are useful for the experimentally-based evaluation of ADME properties for the synthesized novel compounds, supporting the prioritization of drug candidates and guiding further synthesis.     

Lipophilicity in drug discovery

Importance of the field: The role of lipophilicity in determining the overall quality of candidate drug molecules is of paramount importance. Recent developments suggest that, as well as determining pre-clinical ADMET (absorption, distribution, metabolism, elimination and toxicology) properties, compounds of optimal lipophilicity might have increased chances of success in development. Areas covered in this review: The review covers aspects of methods of prediction of lipophilicity in frequent use and describes the most relevant literature analyses linking individual ADMET parameters and more composite measures of overall compound 'quality' with lipophilicity. What the reader will gain: The aim is to provide an overview of the relevant literature in an attempt to summarise where the optimum region of lipophilicity lies and to highlight which particular issues and risks might be expected when operating outside this region. Take home message: The review of the data shows that this optimal space is defined by a narrow range of logD between ～ 1 and 3. Some of the implications of this for medicinal chemistry optimisation are discussed.     

Selecting oral bioavailability enhancing formulations during drug discovery and development

Introduction: The role of chemical structure, lipophilicity, physico-chemical, absorption, distribution, metabolism, excretion, toxicity (ADMET) and biopharmaceutical properties of compounds including bioavailability are critical in drug discovery and drug dosage forms design.

Areas covered: The authors discuss a number of parameters including computational approaches used for selected chemical structures with biological activity for lead optimization and chemogenomics and preclinical studies for ADMET process development of ligand properties. The authors also look at a number of other parameters including: early drug product formulations with method selection based on the biopharmaceutical classification system (BCS); in vitro–in vivo correlation (IVIVC) and different formulation strategies to enhance solubility; dissolution rate and permeability; bioavailability evaluation and quality by design as an opportunity to develop ‘safe space' regions, where bioavailability is unaffected by pharmaceutical variations.

Expert opinion: The biopharmaceutical requirements for absorption are solubility and permeability. Both are influenced by lipophilicity, but in the opposite way. The genomic methodology, coupled with combinatorial chemistry, high-throughput screening, structure-based design and in silico ADMET would yield parameters as a starting point for the biopharmaceutical properties determination in further preclinical and clinical studies. Consecutive stages in drug discovery and development are irreplaceable, but pharmacokinetics is the critical step. Selection of drug formulations based on the BCS, IVIVC are the principal aspects to enhance the solubility and dissolution rate, while a rationale management of pharmaceutical and technological factors will enhance the bioavailability.     

Predictive approaches to increase absorption of compounds during lead optimisation

Introduction: Complex physicochemical and biological processes influence the oral absorption of a drug molecule. Consideration of these processes is an important activity during the optimisation of potential candidate molecules.

Areas covered: The authors review the applications of physicochemical and structural requirements for intestinal absorption. Furthermore, they provide examples of how to aid the lead optimisation process through improvement of solubility and permeability.

Expert opinion: The physicochemical requirements for absorption are solubility and permeability. Both are influenced by lipophilicity, but in the opposite way. The size of the molecule also affects both solubility and permeability. Several models can be used to estimate oral absorption from chemical structure or from measured physicochemical properties. Thus, logD–cMR model, the ‘golden triangle' model, Abraham solvation equations and absorption potential can be used as tools in the lead optimisation process. Measured values of solubility and permeability greatly improve the estimation of in vivo oral absorption of compounds. However, it is important to appreciate that predictions of oral absorption may be confounded by the involvement of active transporters in the gut which may either increase (e.g., active uptake) or decrease (e.g., efflux) the absorption of drug molecules. To evaluate the first-pass metabolism, in vitro clearance measurements using liver microsomes can be used in physiologically based models for the estimation of bioavailability. The general tools discussed in this review are based on the physicochemical property assessment of compound libraries and they help design compounds that occupy desirable property space with increased likelihood of good oral absorption.     

Pharmacophore modelling: applications in drug discovery

This review highlights the concept of using pharmacophore models in modern drug research and reviews some important examples as well as success stories. This includes papers from both method-development and application areas. As indicated by the number of publications available, the pharmacophore approach has proven to be extremely useful not only in virtual screening and library design for efficient hit discovery, but also in the optimisation of lead compounds to clinical candidates. Recent studies focus on the use of parallel screening using pharmacophore models for bioactivity profiling and early stage risk assessment of potential side effects and toxicity, due to the interaction of drug candidates with antitargets.     

The importance of employing computational resources for the automation of drug discovery

Introduction: The application of computational tools to drug discovery helps researchers to design and evaluate new drugs swiftly with a reduce economic resources. To discover new potential drugs, computational chemistry incorporates automatization for obtaining biological data such as adsorption, distribution, metabolism, excretion and toxicity (ADMET), as well as drug mechanisms of action.

Areas covered: This editorial looks at examples of these computational tools, including docking, molecular dynamics simulation, virtual screening, quantum chemistry, quantitative structural activity relationship, principal component analysis and drug screening workflow systems. The authors then provide their perspectives on the importance of these techniques for drug discovery.

Expert opinion: Computational tools help researchers to design and discover new drugs for the treatment of several human diseases without side effects, thus allowing for the evaluation of millions of compounds with a reduced cost in both time and economic resources. The problem is that operating each program is difficult; one is required to use several programs and understand each of the properties being tested. In the future, it is possible that a single computer and software program will be capable of evaluating the complete properties (mechanisms of action and ADMET properties) of ligands. It is also possible that after submitting one target, this computer–software will be capable of suggesting potential compounds along with ways to synthesize them, and presenting biological models for testing.     

Application of drug efficiency index in drug discovery: a strategy towards low therapeutic dose

Introduction: The ultimate objective of optimizing adsorption, distribution, metabolism and excretion (ADME) parameters in drug discovery is to maximize the unbound concentration at the site of action for a given dose level. This has the added benefit of minimizing the efficacious dose, reducing the potential for attrition related to drug burden and direct organ toxicity. The concept of drug efficiency was formulated as a tool to obtain a balanced profile between target affinity and ADME properties during lead optimization.

Areas covered: The authors discuss how it is possible to maximize the in vivo pharmacological potential addressing whether drug efficiency adds value to the decision-making process and whether it is possible to introduce a single optimization parameter, the drug efficiency index (DEI), linking target affinity and ADME properties, as a marker of in vivo efficacy.

Expert opinion: In the absence of a clear hypothesis-driven approach at the beginning of the program (i.e., pharmacokinetic–pharmacodynamic link), the objective to select molecules with a low therapeutic dose is still a major hurdle in drug discovery. The authors believe that a greater strategic focus on mechanistically relevant measures of the determinants of receptor occupancy would help the optimization and selection process. In this respect, the introduction of the DEI, which can be seen as a correction of target affinity by the in vivo pharmacokinetic potential, may help drug discovery to select and promote those molecules with the highest probability to interact with the biological target and with the best balance between target affinity and ADME properties.     

Novel tactics for designing water-soluble molecules in drug discovery

Introduction: Physiochemical drug properties, such as aqueous solubility are considered to be a major factor in determining the ultimate success or failure of experimental agents. Solubility is important because it determines the maximum dose which can be taken up. As the size and hydrophobicity of drug candidates has increased over the years, poor solubility has become a more prevalent issue. Recent examples from the literature show that an improved understanding of the relationship between molecular structure and solubility allows this issue to be approached using rational design.

Areas covered: This review provides selected examples from the recent drug discovery literature that demonstrate various tactics, which have been applied successfully towards improving drug solubility. The examples that were selected demonstrate the underlying principles behind aqueous solubility, such as hydrophobicity and crystalline stability.

Expert opinion: From a strategic point of view, improving the solubility of a compound should be straightforward because it can be accomplished by simply reducing hydrophobicity or crystalline stability. However, the structural elements and physical properties which control solubility also influence potency, pharmacokinetics and toxicity. Furthermore, there are practical aspects such as the quantity and quality of solubility-related data, which hamper the development of structure–solubility relationships. Given that poor aqueous solubility remains a primary issue in drug discovery, there is a continuous need for novel methods to overcome it.     

Lipophilicity in Molecular Modeling

Purpose. The molecular lipophilicity potential (MLP) offers a three-dimensional representation of lipophilicity, a molecular property encoding intermolecular recognition forces and intramolecular interactions. Methods. The interest and applications of the MLP in molecular modeling are varied, as ilustrated here. Results. The MLP is a major tool to assess the dependence of lipophilicity on conformation. As a matter of fact, the MLP combined with an exploration of the conformational space of a solute reveals its "chameleonic behavior, i.e. its capacity to adapt to its molecular environment by hydrophobic collapse or hydrophilic folding. Another successful application of the MLP is its concatenation into 3D-QSAR (Comparative Molecular Field Analysis, CoMFA). Conclusions. Work is in progress to expand the MLP into a docking tool in the modeling of ligand-receptor interactions.     

Understanding mechanisms of toxicity: insights from drug discovery research

Toxicology continues to rely heavily on use of animal testing for prediction of potential for toxicity in humans. Where mechanisms of toxicity have been elucidated, for example endocrine disruption by xenoestrogens binding to the estrogen receptor, in vitro assays have been developed as surrogate assays for toxicity prediction. This mechanistic information can be combined with other data such as exposure levels to inform a risk assessment for the chemical. However, there remains a paucity of such mechanistic assays due at least in part to lack of methods to determine specific mechanisms of toxicity for many toxicants. A means to address this deficiency lies in utilization of a vast repertoire of tools developed by the drug discovery industry for interrogating the bioactivity of chemicals. This review describes the application of high-throughput screening assays as experimental tools for profiling chemicals for potential for toxicity and understanding underlying mechanisms. The accessibility of broad panels of assays covering an array of protein families permits evaluation of chemicals for their ability to directly modulate many potential targets of toxicity. In addition, advances in cell-based screening have yielded tools capable of reporting the effects of chemicals on numerous critical cell signaling pathways and cell health parameters. Novel, more complex cellular systems are being used to model mammalian tissues and the consequences of compound treatment. Finally, high-throughput technology is being applied to model organism screens to understand mechanisms of toxicity. However, a number of formidable challenges to these methods remain to be overcome before they are widely applicable. Integration of successful approaches will contribute towards building a systems approach to toxicology that will provide mechanistic understanding of the effects of chemicals on biological systems and aid in rationale risk assessments.     

人工智能算法用于药物研发的研究进展

人工智能算法包含机器学习算法和深度学习算法,可应用于药物靶标发现、先导化合物的发现与优化、候选药物的确定、成药性优化。人工智能算法通过丰富的大数据系统学习可以实现模型的建立和高通量虚拟计算,应用于药物研发中能够在一定程度上缩短研发周期、降低投入成本,进而提高研发成功率。对机器学习算法、深度学习算法应用于药物研发中的研究进展进行阐述,以期为人工智能技术与药物研发相结合的进一步发展提供参考。     

新药发现阶段药物毒理学研究的策略与方法

随着重大创新药物创制专项的实施,我国创新药物研发的数量将步入稳定上升期。纵观整个药物研发流程,从药物发现、临床前研究、临床研究直到药物上市,药物毒理学研究贯穿始终,并起着非常重要的作用。鉴于创新药物发现阶段药物毒理学地位和作用的日益突出,文中就相关研究的策略与方法作简要阐述。     

Annonaceae: bio-resource for tomorrow's drug discovery

One of the rich sources of lead compounds is the Angiosperms. Many of these lead compounds are useful medicines naturally, whereas others have been used as the basis for synthetic agents. These are potent and effective compounds, which have been obtained from plants, including anti-cancer (cytotoxic) agents, anti-malaria (anti-protozoal) agents, and anti-bacterial agents. Today, the number of plant families that have been extensively studied is relatively very few and the vast majorities have not been studied at all. The Annonaceae is the largest family in the order Magnoliales. It includes tropical trees, bushes, and climbers, which are often used as traditional remedies in Southeast Asia. Members of the Annonaceae have the particularity to elaborate a broad spectrum of natural products that have displayed anti-bacterial, anti-fungal, and anti-protozoal effects and have been used for the treatment of medical conditions, such as skin diseases, intestinal worms, inflammation of the eyes, HIV, and cancer. These special effects and the vast range of variation in potent compounds make the Annonaceae unique from other similar families in the Magnoliales and the Angiosperms in general. This paper attempts to summarize some important information and discusses a series of hypotheses about the effects of Annonaceae compounds.     

Advances in microfluidics for drug discovery

Importance of the field: Microfluidics is considered as an enabling technology for the development of unconventional and innovative methods in the drug discovery process. The concept of micrometer-sized reaction systems in the form of continuous flow reactors, microdroplets or microchambers is intriguing, and the versatility of the technology perfectly fits with the requirements of drug synthesis, drug screening and drug testing.

Areas covered in this review: In this review article, we introduce key microfluidic approaches to the drug discovery process, highlighting the latest and promising achievements in this field, mainly from the years 2007 – 2010.

What the reader will gain: Despite high expectations of microfluidic approaches to several stages of the drug discovery process, up to now microfluidic technology has not been able to significantly replace conventional drug discovery platforms. Our aim is to identify bottlenecks that have impeded the transfer of microfluidics into routine platforms for drug discovery and show some recent solutions to overcome these hurdles.

Take home message: Although most microfluidic approaches are still applied only for proof-of-concept studies, thanks to creative microfluidic research in the past years unprecedented novel capabilities of microdevices could be demonstrated, and general applicable, robust and reliable microfluidic platforms seem to be within reach.     

High-throughput multiplexed capillary electrophoresis in drug discovery

The demand for high-throughput analytical tools to support drug discovery applications has led to the development of multiplexed capillary electrophoresis and multichannel microfluidic devices to characterize libraries of compounds and alleviate backlogs in the discovery process. The capability to analyze multiple samples in parallel, and the diverse separation conditions that are permissible, facilitates rapid turnaround times. Examples of high-throughput applications of multiplexed electrophoresis in drug discovery include: physicochemical profiling, enzyme analysis, chiral separations and protein/metabolite analysis. Many single capillary electrophoresis methods can be potentially adapted to a multiplexed format, therefore, we anticipate the development of other high-throughput applications in the near future, which should facilitate decreases in sample analysis time and help improve laboratory efficiency.     

The role of early in vivo toxicity testing in drug discovery toxicology

The cost impact of late-stage failures of drug candidates has motivated the pharmaceutical industry to develop, validate, and implement a more proactive testing paradigm, including an emphasis on conducting predictive in vitro and in vivo studies earlier. The goal of drug discovery toxicology is not to reduce or eliminate attrition, as is often mis-stated as such, but rather to reprioritize efforts to shift attrition of future failing molecules upstream in discovery. This shift in attrition requires additional studies and investment earlier in the candidate evaluation process in order to avoid spending resources on molecules with soon-to-be-discovered development-limiting liabilities. While in silico and in vitro models will continually be developed and refined, in vivo preclinical safety models remain the gold standard for assessing human risk. For in vivo testing to influence early discovery effectively, it must: i) require low amounts of compound; ii) provide rapid results to drive decision-making and medicinal chemistry efforts; and iii) be flexible and provide results relevant to the development plan tailored to each target, drug class, and/or indication.     

Identifying targets for drug discovery using bioinformatics

Background: Drug discovery is the process of discovering and designing drugs, which includes target identification, target validation, lead identification, lead optimization and introduction of the new drugs to the public. This process is very important, involving analyzing the causes of the diseases and finding ways to tackle them. Objective: The problems we must face include: i) that this process is so long and expensive that it might cost millions of dollars and take a dozen years; and ii) the accuracy of identification of targets is not good enough, which in turn delays the process. Introducing bioinformatics into the drug discovery process could contribute much to it. Bioinformatics is a booming subject combining biology with computer science. It can explore the causes of diseases at the molecular level, explain the phenomena of the diseases from the angle of the gene and make use of computer techniques, such as data mining, machine learning and so on, to decrease the scope of analysis and enhance the accuracy of the results so as to reduce the cost and time. Methods: Here we describe recent studies about how to apply bioinformatics techniques in the four phases of drug discovery, how these techniques improve the drug discovery process and some possible difficulties that should be dealt with. Results: We conclude that combining bioinformatics with drug discovery is a very promising method although it faces many problems currently.