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.     

Practical metabolomics in drug discovery

Importance of the field: Metabolomics is increasingly becoming an important field in the pharmaceutical industry to support the discovery and development of therapeutic agents. It allows the comprehensive and simultaneous profiling of hundreds of discrete biologically important molecules, including amino acids, sugars, lipids and exogenous substances from biological fluids and tissues. Metabolomics is the ‘omics’ field that most represents the interplay of internal biological regulation and external environmental influences on disease, thereby being of particular importance to disease mitigation and management.

Areas covered in this review: Technological advances in the experimental work flow, analytical detection strategies and bioinformatics tools have enabled metabolomics studies to become increasingly comprehensive, robust and informative for the understanding of disease, drug action and the development of biomarkers. This review will focus on the practical aspects of metabolomics studies as they have been applied to the study of mammalian biological systems, specifically targeted to the steps of experimental design with regard to sample preparation, sample analysis and data analysis of both polar and non-polar metabolites.

What the reader will gain: The reader will gain an overview of the field of metabolomics as it applies to drug development and the practical issues involved with experimental design. We will discuss the various methods of sample preparation and analysis as they apply to different classes of metabolites and highlight recent advances in the field that illustrate these methods.

Take home message: The field of metabolomics is a rapidly expanding discipline that is being applied to various aspects of drug development. The large diversity of metabolites found in nature dictates that different methods be developed for the investigation of different classes of metabolites. As the field of metabolomics continues to mature, it is likely that it will play an increasingly important role in the characterization of disease and the future development of biomarkers to assess drug efficacy and safety.     

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.     

Providing data science support for systems pharmacology and its implications to drug discovery

ABSTRACT

Introduction: The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology.

Areas covered: This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction.

Expert opinion: Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.     

Opportunities to minimise risk in drug discovery and development

ABSTRACT

Introduction: Artificial neural networks (ANNs) are highly adaptive nonlinear optimization algorithms that have been applied in many diverse scientific endeavors, ranging from economics, engineering, physics, and chemistry to medical science. Notably, in the past two decades, ANNs have been used widely in the process of drug discovery.

Areas covered: In this review, the authors discuss advantages and disadvantages of ANNs in drug discovery as incorporated into the quantitative structure-activity relationships (QSAR) framework. Furthermore, the authors examine the recent studies, which span over a broad area with various diseases in drug discovery. In addition, the authors attempt to answer the question about the expectations of the ANNs in drug discovery and discuss the trends in this field.

Expert opinion: The old pitfalls of overtraining and interpretability are still present with ANNs. However, despite these pitfalls, the authors believe that ANNs have likely met many of the expectations of researchers and are still considered as excellent tools for nonlinear data modeling in QSAR. It is likely that ANNs will continue to be used in drug development in the future.     

Novel routes to bipolar disorder drug discovery

Introduction: Bipolar disorder (BD) is a severe and chronic medical condition typified by episodic recurrent mania (or hypomania) in addition to major depression. BD is associated with a number of negative outcomes including premature death, reduced quality of life and can also lead to other complications including impaired cognitive function. Unfortunately, the currently available pharmacological treatments for BD are insufficient for many with the condition.

Areas covered: This review focuses on known therapeutic targets of mood stabilizing drugs including: the glycogen synthase kinase-3 (GSK-3), the phosphoinositide pathway and protein kinase C (PKC), the brain-derived neurotrophic factor (BDNF), and histone deacetylases (HDACs). This article also presents new promising therapeutic targets including: the glutamatergic pathway, mitochondrial modulators, neuropeptide-converting endopeptidases, the insulin transduction pathway, the purinergic system and the melatoninergic system.

Expert opinion: Challenges in improving methods and tools to generate, integrate and analyze high-dimensional data are required to allow opening novel routes to BD drug discovery. Through the application of systems biology approaches and the use of bioinformatical tools to integrate all omics data, it will be possible in the near future to gain deeper insights into pathophysiology of BD. This will in turn lead to the identification and exploitation of new potential therapeutic approaches.     

The importance of chronobiology to drug discovery

Drug discovery scientists, faced with the myriad challenges involved in developing novel therapeutics as medicines, have tended to overlook the question of the most beneficial time to administer the drug. Recent developments in our understanding of circadian biology and the availability of tools to characterise the molecular clock indicate that time and duration of dosing may have profound consequences for the efficacy and safety of new and existing therapeutic agents. Progress in the field also suggests that many key physiological mechanisms are remarkably dependent on the circadian clock. It has also become clear that a number of diseases with important unmet medical need display marked circadian variation in their symptoms and severity. These discoveries now reveal opportunities for new therapeutic strategies to be developed that act by modulation of biological rhythms. These novel therapeutic approaches are likely to be facilitated by the continuing development of chemical probes and synthetic ligands targeted to an increasing number of the key proteins that regulate the molecular clock.     

Structure-based drug discovery and protein targets in the CNS

Structure-based methods are having an increasing role and impact in drug discovery. The crystal structures of an increasing number of therapeutic targets are becoming available. These structures can transform our understanding of how these proteins perform their biological function and often provide insights into the molecular basis of disease. In addition, the structures can help the discovery process. Methods such as virtual screening and experimental fragment screening can provide starting hit compounds for a discovery project. Crystal structures of compounds bound to the protein can direct or guide the medicinal chemistry optimisation to improve drug-like properties - not only providing ideas on how to improve binding affinity or selectivity, but also showing where the compound can be modified in attempting to modulate physico-chemical properties and biological efficacy. The majority of drug discovery projects against globular protein targets now use these methods at some stage.This review provides a summary of the range of structure-based drug discovery methods that are in use and surveys the suitability of the methods for targets currently identified for CNS drugs. Until recently, structure-based discovery was difficult or unknown for these targets. The recent determination of the structures of a number of GPCR proteins, together with the steady increase in structures for other membrane proteins, is opening up the possibility for these structure-based methods to find increased use in drug discovery for CNS diseases and conditions.     

The clinical pharmacologist in drug discovery and development

1 Clinical pharmacology is a key activity in drug discovery and drug development with much to contribute to drug innovation.
2 However, very few clinical pharmacologists choose the pharmaceutical industry as their ultimate career.
3 Medical alumni of the RPMS clinical pharmacology department illustrate this; only four industrial careers vs thirty professors of clinical pharmacology or medicine.     

From blood to brain: blood cell-based biomimetic drug delivery systems

Brain drug delivery remains a major difficulty for several challenges including the blood–brain barrier, lesion spot targeting, and stability during circulation. Blood cells including erythrocytes, platelets, and various subpopulations of leukocytes have distinct features such as long-circulation, natural targeting, and chemotaxis. The development of biomimetic drug delivery systems based on blood cells for brain drug delivery is growing fast by using living cells, membrane coating nanotechnology, or cell membrane-derived nanovesicles. Blood cell-based vehicles are superior delivery systems for their engineering feasibility and versatile delivery ability of chemicals, proteins, and all kinds of nanoparticles. Here, we focus on advances of blood cell-based biomimetic carriers for from blood to brain drug delivery and discuss their translational challenges in the future.     

化学蛋白质组学与药物靶点的发现

小分子药物靶点的发现对于生物和医学的研究者而言,是一项既重要又艰巨的任务,医学和药学界研究工作者急切需要发现和确认新的靶点。为了克服药物靶点确认的瓶颈,已经发展了许多新技术用以研究小分子化合物与蛋白质分子间的相互作用,其中包括化学蛋白质组学方法。化学蛋白质组是全蛋白质组学研究的一个亚类,化学蛋白质组学是利用能够与靶蛋白质发生特异性相互作用的化学小分子来干扰和探测蛋白质组,在分子水平上系统揭示特定蛋白质的功能以及蛋白质与化学小分子的相互作用,从而准确找到药物作用靶点的组学研究方法。化学蛋白质组学技术和方法不断成熟,在药物作用靶点的发现、确认和药物多靶点研究等方面都将起到重要的作用,并将大大提高药物发现的效率。     

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.     

整合新模式提高药物发现效率

为了消除新药研究与开发的瓶颈和提高效率,各制药公司正在对药物发现与开发的所有阶段进行评估,包括从靶的证实和化合物的设计到临床试验和生产过程。各制药公司都在积极采用各种新方法加速新药研究与开发的进程,整合药物开发的新模式势在必行。     

Liquid chromatography coupled to mass spectrometry for metabolite profiling in the field of drug discovery

Introduction: The identification and characterization of the metabolites during the early stages of discovery and development of new drug candidates are essential to establish the metabolic clearance as well as the potential pharmacological and/or toxicological effects. Hence, feasible methods of analysis, preferably rapid and simple, are required to satisfy the increasing demand of metabolite profiling studies.

Areas covered: This paper reviews the topic of metabolite profiling in drug discovery based on liquid chromatography, with especial emphasis on chromatographic modes and detectors. Features and possibilities of the different options are critically discussed.

Expert opinion: High performance analytical techniques are fundamental to gain unambiguous information on metabolites of new drugs. In this regard, liquid chromatography hyphenated to mass spectrometric detection is the most popular approach. The diversity of chromatographic modes and the great variety of separation columns available offer innumerable analytical possibilities to characterize and quantify compounds with a broad range of physicochemical properties.     

Ligand-directed profiling: applications to target drug discovery in cancer

Background: Generation of targeted therapy remains a major challenge in medicine. The development of drugs that can discriminate between tumor cells and non-malignant cells would improve efficacy and reduce general side effects. Phage display allows identification of specific supramolecular complexes that can target therapeutic compounds or imaging agents, both in vitro and in vivo. The use of phage display to identify molecules expressed on the surface of human cancer cells without bias, as well as to provide initial steps toward identification of a ligand/receptor-based map of the human microvasculature, has broad implications for drug discovery in general, especially for cancer therapy. Objective/method: In this review, we discuss the use of phage display technology as a ligand-directed targeting strategy and its applications to drug discovery. Conclusion: Compared to other existing drug discovery platforms, phage display technology has the advantage to provide valuable clues pointing to target proteins in an unbiased biological context. The result from various display library screenings indicates that in many cases the selected peptide motifs mimic biological ligands. Analysis of peptide motifs targeting a receptor provides a basis for rational drug design of targeted peptidomimetics.     

Explainability and white box in drug discovery

Recently, artificial intelligence (AI) techniques have been increasingly used to overcome the challenges in drug discovery. Although traditional AI techniques generally have high accuracy rates, there may be difficulties in explaining the decision process and patterns. This can create difficulties in understanding and making sense of the outputs of algorithms used in drug discovery. Therefore, using explainable AI (XAI) techniques, the causes and consequences of the decision process are better understood. This can help further improve the drug discovery process and make the right decisions. To address this issue, Explainable Artificial Intelligence (XAI) emerged as a process and method that securely captures the results and outputs of machine learning (ML) and deep learning (DL) algorithms. Using techniques such as SHAP (SHApley Additive ExPlanations) and LIME (Locally Interpretable Model-Independent Explanations) has made the drug targeting phase clearer and more understandable. XAI methods are expected to reduce time and cost in future computational drug discovery studies. This review provides a comprehensive overview of XAI-based drug discovery and development prediction. XAI mechanisms to increase confidence in AI and modeling methods. The limitations and future directions of XAI in drug discovery are also discussed.     

细胞膜仿生纳米载体的制备及应用研究进展

由有机或无机纳米材料制备的药物载体系统广泛用于药物靶向递送和疾病的诊断治疗研究。但其存在靶向性差、体内循环时间短、生物相容性欠佳亟需提高等问题。仿生纳米药物系统是以不同种类的细胞膜修饰纳米载体,利用内源性的细胞膜提高载体的体内生物相容性、实现更精准的靶向、甚至由细胞自身的免疫原性产生免疫治疗作用。对细胞膜仿生纳米载体技术的原理、方法及其靶向机制和治疗作用作一综述,为新型给药系统研究提供思路。