Search

This review presents the main aspects related to pharmacokinetic properties, which are essential for the efficacy and safety of drugs. This topic is very important because the analysis of pharmacokinetic aspects in the initial design stages of drug candidates can increase the chances of success for the entire process. In this scenario, experimental and in silico techniques have been widely used. Due to the difficulties encountered with the use of some experimental tests to determine pharmacokinetic properties, several in silico tools have been developed and have shown promising results. Therefore, in this review, we address the main free tools/servers that have been used in this area, as well as some cases of application. Finally, we present some studies that employ a multidisciplinary approach with synergy between in silico, in vitro, and in vivo techniques to assess ADME properties of bioactive substances, achieving successful results in drug discovery and design., (© 2021 Wiley-VCH GmbH.)

The current manufacturing solutions for oral solid dosage forms are fundamentally based on technologies from the 19th century. This approach is well suited for mass production of one-size-fits-all products; however, it does not allow for a straight-forward personalization and mass customization of the pharmaceutical end-product. In order to provide better therapies to the patients, a need for innovative manufacturing concepts and product design principles has been rising. Additive manufacturing opens up a possibility for compartmentalization of drug products, including design of spatially separated multidrug and functional excipient compartments. This compartmentalized solution can be further expanded to modular design thinking. Modular design is referring to combination of building blocks containing a given amount of drug compound(s) and related functional excipients into a larger final product. Implementation of modular design principles is paving the way for implementing the emerging personalization potential within health sciences by designing compartmental and reactive product structures that can be manufactured based on the individual needs of each patient. This review will introduce the existing compartmentalized product design principles and discuss the integration of these into edible electronics allowing for innovative control of drug release., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Additive manufacturing (AM) or 3D printing is enabling new directions in product design. The adoption of AM in various industrial sectors has led to major transformations. Similarly, AM presents new opportunities in the field of drug delivery, opening new avenues for improved patient care. In this review, we discuss AM as an innovative tool for drug product design. We provide a brief overview of the different AM processes and their respective impact on the design of drug delivery systems. We highlight several enabling features of AM, including unconventional release, customization, and miniaturization, and discuss several applications of AM for the fabrication of drug products. This includes products that have been approved or are in development. As the field matures, there are also several new challenges to broad implementation in the pharmaceutical landscape. We discuss several of these from the regulatory and industrial perspectives and provide an outlook for how these issues may be addressed. The introduction of AM into the field of drug delivery is an enabling technology and many new drug products can be created through productive collaboration of engineers, materials scientists, pharmaceutical scientists, and industrial partners., Competing Interests: Declaration of Competing Interest The authors declare the following potential competing financial interests. H.R., S.R., P.D.W., and G.S. are employees of F. Hoffmann-La Roche AG. E.A.G., M.W.T., S.R., and G.S. are co-inventors on patents related to additive manufacturing of drug delivery systems. For a list of entities with which R.L. is involved, compensated or uncompensated, see www.dropbox.com/s/yc3xqb5s8s94v7x/Rev%20Langer%20COI.pdf?dl=0., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Orodispersible films (ODFs) are ultra-thin, stamp-sized, elegant, portable and patient-centric pharmaceutical dosage forms that do not need water to be ingested. They are particularly useful for paediatric and geriatric patient populations with special needs such as dysphagia, Parkinson's disease, and oral cancer. Accordingly, they hold tremendous potential in gaining patient compliance, convenience and pharmacotherapy. In the present review, conception and evolution of ODFs as a product and its technology are discussed. The review continues by providing overview about the potential of ODFs as carriers for delivering drugs, herbal extracts, probiotics and vaccines. Besides, strategies employed in drug cargo loading, taste masking of bitter drugs and enhancing drug stability are discussed. Finally, the review concludes by providing a brief overview about quality by design (QbD) principles in development of ODFs., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Acylhydrazones are still an important framework to the design of new bioactive compounds. As treatment of chronic pain represents a clinical challenge, we decided to modify the structure of LASSBio-1514 ( 1 ), previously described as anti-inflammatory and analgesic prototype. Applying the homologation as a strategy for molecular modification, we designed a series of cyclopentyl- ( 2a - e ), cyclobutyl- ( 3a - e ), and cyclopropylacylhydrazones ( 4a - e ) that were synthetized and evaluated in murine models of inflammation and pain. A comparison of their in silico physicochemical and drug-like profile was conducted, as well as their anti-inflammatory and analgesic effect. Compounds 4a (LASSBio-1755) and 4e (LASSBio-1757) displayed excellent in silico drug-like profiles and were identified as new analgesic lead-candidates in acute and chronic model of pain, through oral administration.

Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generative model that bridges systems biology and molecular design, conditioning a generative adversarial network with transcriptomic data. By doing so, we can automatically design molecules that have a high probability to induce a desired transcriptomic profile. As long as the gene expression signature of the desired state is provided, this model is able to design active-like molecules for desired targets without any previous target annotation of the training compounds. Molecules designed by this model are more similar to active compounds than the ones identified by similarity of gene expression signatures. Overall, this method represents an alternative approach to bridge chemistry and biology in the long and difficult road of drug discovery.

Background: Several medicinal plants are being used in Indian medicine systems from ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known., Objective: This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach., Methods: We built a database of the Indian medicinal and aromatic plants coupled with associated information (plant families, plant parts used for the medicinal purpose, structural information, therapeutic properties, etc.) We also developed a Java-based chemoinformatics open-source tool called DoMINE (Database of Medicinally Important Natural products from plantaE) for the generation of virtual library and screening of novel molecules from known medicinal plant molecules. We employed chemoinformatics approaches to in-silico screened metabolites from 104 Indian medicinal and aromatic plants and designed novel drug-like bioactive molecules. For this purpose, 1665 ring containing molecules were identified by text mining of literature related to the medicinal plant species, which were later used to extract 209 molecular scaffolds. Different scaffolds were further used to build a focused virtual library. Virtual screening was performed with cluster analysis to predict drug-like and lead-like molecules from these plant molecules in the context of drug discovery. The predicted drug-like and lead-like molecules were evaluated using chemoinformatics approaches and statistical parameters, and only the most significant molecules were proposed as the candidate molecules to develop new drugs., Results and Conclusion: The supra network of molecules and scaffolds identifies the relationship between the plant molecules and drugs. Cluster analysis of virtual library molecules showed that novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. We also developed the DoMINE toolkit for the advancement of natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. Hence, this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values. These synthesized molecules need to be subjected to biological screening to identify potential molecules for drug discovery research., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

New drugs introduced to the market every year represent privileged structures for particular biological targets. These new chemical entities (NCEs) provide insight into molecular recognition while serving as leads for designing future new drugs. This annual review describes the most likely process-scale synthetic approaches to 31 new chemical entities approved for the first time globally in 2017.

Background: Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug., Methods: In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan., Results: A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given., Conclusion: The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Route Design is the first step in the strategic development process for manufacture of a new active pharmaceutical ingredient (API). Numerous benefits can be realised for the project and the broader performance of the company. We present an appreciation of some of the potential challenges and describe the principles and practices that have shaped the Route Design culture within AstraZeneca. This is exemplified with case histories and we describe some of the activities that have supported our scientists and the simple messages used to educate the broader organisation., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Over the past ten years, the number of three-dimensional protein structures identified by advanced science and technology increases, and the gene information becomes more available than ever before as well. The development of computing science becomes another driving force which makes it possible to use computational methods effectively in various phases of the drug design and research. Now Structure-Based Drug Design (SBDD) tools are widely used to help researchers to predict the position of small molecules within a three-dimensional representation of the protein structure and estimate the affinity of ligands to target protein with considerable accuracy and efficiency. They also accelerate discovery speed of potent drug and reduce the cost and times for drug research. Here we present an overview of SBDD used in drug discovery and highlight its recent successes and major challenges to current SBDD methodologies., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Background: Three-dimensional printing (3DP) is a novel technology for fabrication of personalized medicine. As of late, FDA affirmed 3D printed tranquilize item in August 2015, which is characteristic of another section of Pharmaceutical assembling. 3DP incorporates a wide range of assembling procedures, which are altogether founded on computer-aided design (CAD), and controlled deposition of materials (layer-by-layer) to make freestyle geometries. Conventionally, many pharmaceutical processes like compressed tablet have been used from many years for the development of tablet with established regulatory pathways. But this simple process is outdated in terms of process competence and manufacturing flexibility (design space). 3DP is a new technology for the creation of plan, proving to be superior for complex products, customized items and items made on-request. It creates new opportunities for improving efficacy, safety, and convenience of medicines., Method: There are many of the 3D printing technology used for the development of personalized medicine on demand for better treatment like 3D powder direct printing technology, fused-filament 3D printing, 3D extrusion printer, piezoelectric inkjet printer, fused deposition 3D printing, 3D printer, ink-jet printer, micro-drop inkjet 3DP, thermal inkjet printer, multi-nozzle 3D printer, stereolithographic 3D printer., Result: This review highlights features how item and process comprehension can encourage the improvement of a control technique for various 3D printing strategies., Conclusion: It is concluded that the 3D printing technology is a novel potential for manufacturing of personalized dose medicines, due to better patient compliance which can be prepared when needed., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

To conduct organic synthesis in the field of pharmaceutical science, methodologies that can easily and quickly supply compounds with high drug-likeness are highly desirable. Based on the original catalyst design concept "Radical-Conjugated Redox Catalysis (RCRC)" established during my research, various C(sp 3 )-H functionalizations and protein modifications have been developed, taking advantage of the high reactivity and chemoselectivity of the single-electron transfer process. This review focuses on the eight-year research efforts by my collaborators and me, from conception to results.

We present a comprehensive review of the advent and impact of continuous flow chemistry with regard to the synthesis of natural products and drugs, important pharmaceutical products and definitely responsible for a revolution in modern healthcare. We detail the beginnings of modern drugs and the large scale batch mode of production, both chemical and microbiological. The introduction of modern continuous flow chemistry is then presented, both as a technological tool for enabling organic chemistry, and as a fundamental research endeavor. This part details the syntheses of bioactive natural products and commercial drugs.

Introduction: PPIs are involved in every disease and specific modulation of these PPIs with small molecules would significantly improve our prospects of developing therapeutic agents. Both industry and academia have engaged in the identification and use of PPI inhibitors. However in comparison, the opposite strategy of employing small-molecule stabilizers of PPIs is underrepresented in drug discovery. Areas covered: PPI stabilization has not been exploited in a systematic manner. Rather, this concept validated by a number of therapeutically used natural products like rapamycin and paclitaxel has been shown retrospectively to be the basis of the activity of synthetic molecules originating from drug discovery projects among them lenalidomide and tafamidis. Here, the authors cover the growing number of synthetic small-molecule PPI stabilizers to advocate for a stronger consideration of this as a drug discovery approach. Expert opinion: Both the natural products and the growing number of synthetic molecules show that PPI stabilization is a viable strategy for drug discovery. There is certainly a significant challenge to adapt compound libraries, screening techniques and downstream methodologies to identify, characterize and optimize PPI stabilizers, but the examples of molecules reviewed here in our opinion justify these efforts.

The availability of suitable diverse fragment- and lead-oriented screening compounds is key for the identification of suitable chemical starting points for drug discovery programs. The physicochemical properties of molecules are crucial in determining the success of small molecules in clinical development, yet reports suggest that pharmaceutical and academic sectors often produce molecules with poor drug-like properties. We present a platform to design novel, high quality and diverse fragment- and lead-oriented libraries with appropriate physicochemical properties in a cost-efficient manner. This approach has the potential to assist the way libraries are constructed by significantly addressing the historical uneven exploration of chemical space for drug discovery. Additionally, this platform can teach undergraduates and graduates about compound library design., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Nature provides a valuable resource of medicinally relevant compounds, with many antimicrobial and antitumor agents entering clinical trials being derived from natural products. The generation of analogues of these bioactive natural products is important in order to gain a greater understanding of structure activity relationships; probing the mechanism of action, as well as to optimise the natural product's bioactivity and bioavailability. This chapter critically examines different approaches to generating natural products and their analogues, exploring the way in which synthetic and biosynthetic approaches may be blended together to enable expeditious access to new designer natural products.

The translation of nanomedicines from concepts to commercial products has not reached its full potential, in part because of the technical and regulatory challenges associated with chemistry, manufacturing, and controls (CMC) development of such complex products. It is critical to take a quality by design (QbD) approach to developing nanomedicines-using a risk-based approach to identifying and classifying product attributes and process parameters and ultimately developing a deep understanding of the products, processes, and platform. This article exemplifies a QbD approach used by BIND Therapeutics, Inc., to industrialize a polymeric targeted nanoparticle drug delivery platform. The focus of the approach is on CMC affairs but consideration is also given to preclinical, clinical, and regulatory aspects of pharmaceutical development. Processes are described for developing a quality target product profile and designing supporting preclinical studies, defining critical quality attributes and process parameters, building a process knowledge map, and employing QbD to support outsourced manufacturing.

Aim: Many medicinal chemistry-relevant structures and core scaffolds tend toward geometric planarity, which hampers the optimization of physicochemical properties desirable in drug-like molecules. As challenging drug target classes emerge, the exploitation of molecular three-dimensionality in lead optimization is becoming increasingly important. While recent interest has emphasized the importance of enhanced three-dimensionality in molecular fragment designs, the extent to which this is required in core scaffolds remains unclear., Materials & Methods: Three computational methods, Scaffold Tree deconstruction, Synthetic Disconnection Rules retrosynthetic deconstruction and virtual library enumeration, are applied, together with the descriptors plane of best fit and principal moments of inertia, to investigate the origins of three-dimensionality in drug-like molecules., Conclusion: This study informs on the stage at which molecular three-dimensionality should be considered in drug design.

Background: Polypharmacology plays an important role in drug discovery. Polypharmacology drugs strategy provides a novel way in drug design. However, to develop a polypharmacology drug with desired profile remains a challenge., Methods: Owing to the huge progress in computational biology and chemistry, the rational drug design is becoming increasingly important in discovery of polypharmacology drug., Results: Several methodologies on the rational polypharmacology drug design have been developed, which are summarized and classified as ligand based design in polypharmacology, target based design in polypharmacology, and the hybrid of ligand and target based design in polypharmacology., Conclusion: We give an overview of the importance of polypharmacology in drug design and current trends in rational design of polypharmacology, which may be beneficial to the design and development of polypharmacology drugs.

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Due to the time and effort requirements for the development of a new drug, and the high attrition rates associated with this developmental process, there is an intense effort by academic and industrial researchers to find novel ways for more effective drug development schemes. The first step in the discovery process of a new drug is the identification of the lead compound. The modern research tendency is to avoid the synthesis of new molecules based on chemical intuition, which is time and cost consuming, and instead to apply in silico rational drug design. This approach reduces the consumables and human personnel involved in the initial steps of the drug design. In this review real examples from our research activity aiming to discover new leads will be given for various dire warnings diseases. There is no recipe to follow for discovering new leads. The strategy to be followed depends on the knowledge of the studied system and the experience of the researchers. The described examples constitute successful and unsuccessful efforts and reflect the reality which medicinal chemists have to face in drug design and development. The drug stability is also discussed in both organic molecules and metallotherapeutics. This is an important issue in drug discovery as drug metabolism in the body can lead to various toxic and undesired molecules.

Pharmacological activity and druggability are two pivotal factors in drug innovation, which are respectively determined by the microscopic structure and macroscopic property of a molecule. Since structural optimization consists in a molecular operation in the space with multi-dimensions, and there exists a body of uncertainties for transduction from in vitro activity into in vivo pharmacological response. It is necessary for early stage in lead optimization to evaluate compound quality or efficiency using a kind of metrics containing multi-parameters. On the basis of the describing parameters of activity and druggability, this overview deals with the roles of thermodynamic signatures and binding kinetics of drug-receptor interactions in optimizing quality of compounds, signifying the significance in optimization of microscopic structures for drug discovery.

Lead compound optimization plays an important role in new drug discovery and development. The strategies for changing metabolic pathways can modulate pharmacokinetic properties, prolong the half life, improve metabolism stability and bioavailability of lead compounds. The strategies for changing metabolic pathways and improving metabolism stability are reviewed. These methods include blocking metabolic site, reduing lipophilicity, changing ring size, bioisosterism, and prodrug.

The methyl group plays an important role in the rational drug design. Introducing methyl into small molecules has become an important strategy of lead compound optimization. The application of methyl in drug design is reviewed in this paper. Methyl can modulate the physicochemical, pharmacodynamic, and pharmacokinetic properties by ortho effect, inductive effect, and conformational effect. It also improves the metabolic stability as a soft metabolic point. In addition, introducing methyl into drug molecules can also be applied as a strategy in new uses of old drugs and generate me-too drugs.

New drugs are introduced to the market every year and each represents a privileged structure for its biological target. These new chemical entities (NCEs) provide insights into molecular recognition and also serve as leads for designing future new drugs. This review covers the synthesis of 26 NCEs that were launched in the world in 2011., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Despite the tremendous progress in the field of drug designing, discovering a new drug molecule is still a challenging task. Drug discovery and development is a costly, time consuming and complex process that requires millions of dollar and 10-15 years to bring new drug molecules in the market. This huge investment and long-term process are attributed to high failure rate, complexity of the problem and strict regulatory rules, in addition to other factors. Given the availability of 'big' data with ever improving computing power, it is now possible to model systems which is expected to provide time and cost effectiveness to drug discovery process. Computer Aided Drug Designing (CADD) has emerged as a fast alternative method to bring down the cost involved in discovering a new drug. In past, numerous computer programs have been developed across the globe to assist the researchers working in the field of drug discovery. Broadly, these programs can be classified in three categories, freeware, shareware and commercial software. In this review, we have described freeware or open-source software that are commonly used for designing therapeutic molecules. Major emphasis will be on software and web services in the field of chemo- or pharmaco-informatics that includes in silico tools used for computing molecular descriptors, inhibitors designing against drug targets, building QSAR models, and ADMET properties.

The tissue distribution of a drug can have significant impact on both its efficacy and safety. As a consequence, selective tissue targeting has become an attractive approach for optimizing the window between efficacy and safety for drug targets that are ubiquitously expressed and important in key physiological processes. Given the liver's key role in metabolic regulation and the fact that it is the principal tissue affected by diseases such as hepatitis B and C viruses as well as hepatocellular carcinoma, designing drugs with hepatoselective distribution profiles is an important strategy in developing safe cardiovascular, metabolic, antiviral and oncology drug candidates. In this paper, we analyze a diverse set of compounds from four different projects within Pfizer that specifically pursued liver targeting strategies. A number of key in vitro and in vivo ADME endpoints were collected including in vivo tissue exposure, oral bioavailability, clearance in preclinical species and in vitro hepatic OATP uptake, in vitro rat liver microsomal stability, permeability, solubility, logD, and others. From this analysis, we determined a set of general structure-liver-selectivity guides for designing orally bioavailable, liver-targeted candidates using liver specific OATP transporters. The guidelines have been formulated using straightforward molecular descriptors and in vitro properties that medicinal chemists routinely optimize. Our analysis emphasizes the need to focus on a chemical space with balanced lipophilicity, high aqueous solubility and low passive permeability in order to achieve the desired hepatoselectivity while maintaining fraction absorbed.

Chemistry groups involved in drug discovery continue to devote their efforts to improving compound design with the aim of identifying new drug candidates. Many crucial factors must be considered, including: chemical stability, synthetic difficulty, chemical complexity and diversity, ADMET properties, cost, chemical novelty and intellectual property issues, and 'biological appropriateness'. With regard to the latter point, natural products offer an outstanding source of biologically active molecules that provide many useful features that enable us to design innovative, biologically biased, synthetic compounds. This article outlines the recent approaches in this area and suggests a simple metric to assess synthetic compounds for natural product likeness., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

New drugs are introduced to the market every year and each represents a privileged structure for its biological target. These new chemical entities (NCEs) provide insights into molecular recognition and also serve as leads for designing future new drugs. This review covers the synthesis of 15 NCEs that were launched anywhere in the world in 2010., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Natural products represent an important treasure box of biologically active molecules, from which many drug candidates have been sourced. The identification of the target proteins addressed by these natural products is a foremost goal for which new techniques are required. Activity-based protein profiling (ABPP), exploiting protein-reactive functional groups present in many natural products, offers unseen opportunities in this respect. This review article describes the current status of this field. Many examples are given for the annotation of biological target proteins of natural products containing epoxides, lactones, lactams, Michael acceptors, and other electrophilic groups. In addition, the development of probe molecules identified from biomimetic natural product libraries is discussed.

To an outsider, the exploration of thousands of molecules to find a small number of potential candidate drugs must appear enormously wasteful, but many medicinal chemists would defend this waste as unavoidable. Here, I provide evidence that suggests that modern medicinal chemists are overproductive in that they synthesise many more compounds than are required to achieve the objectives of the project. The difficulties encountered in finding the data for the analysis presented here prompted the design and implementation of a more rigorous approach to capture the essence of a medicinal chemistry program. The result, medicinal chemistry knowledge sharing (MeCKS), was designed to capture and communicate emerging issues and their solutions to the medicinal chemistry community., (Copyright © 2011. Published by Elsevier Ltd.)

The impact of carboaromatic, heteroaromatic, carboaliphatic and heteroaliphatic ring counts and fused aromatic ring count on several developability measures (solubility, lipophilicity, protein binding, P450 inhibition and hERG binding) is the topic for this review article. Recent results indicate that increasing ring counts have detrimental effects on developability in the order carboaromatics≫heteroaromatics>carboaliphatics>heteroaliphatics, with heteroaliphatics exerting a beneficial effect in many cases. Increasing aromatic ring count exerts effects on several developability parameters that are lipophilicity- and size-independent, and fused aromatic systems have a beneficial effect relative to their nonfused counterparts. Increasing aromatic ring count has a detrimental effect on human bioavailability parameters, and heteroaromatic ring count (but not other ring counts) has increased over time in marketed oral drugs., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

New drugs are introduced to the market every year and each individual drug represents a privileged structure for its biological target. These new chemical entities (NCEs) provide insights into molecular recognition and also serve as leads for designing future new drugs. This review covers the syntheses of 21 NCEs marketed in 2009., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Computer-assisted molecular design supports drug discovery by suggesting novel chemotypes and compound modifications for lead structure optimization. While the aspect of synthetic feasibility of the automatically designed compounds has been neglected for a long time, we are currently witnessing an increased interest in this topic. Here, we review state-of-the-art software for de novo drug design with a special emphasis on fragment-based techniques that generate druglike, synthetically accessible compounds. The importance of scoring functions that can be used to predict compound reactivity and potency is highlighted, and several promising solutions are discussed. Recent practical validation studies are presented that have already demonstrated that rule-based fragment assembly can result in novel synthesizable compounds with druglike properties and a desired biological activity.

In addition to being responsible for the majority of absorption of dietary nitrogen, the mammalian proton-coupled di- and tri-peptide transporter PepT1 is also recognised as a major route of drug delivery for several important classes of compound, including beta-lactam antibiotics and angiotensin-converting enzyme inhibitors. Thus there is considerable interest in the PepT1 protein and especially its substrate binding site. In the absence of a crystal structure, computer modelling has been used to try to understand the relationship between PepT1 3D structure and function. Two basic approaches have been taken: modelling the transporter protein, and modelling the substrate. For the former, computer modelling has evolved from early interpretations of the twelve transmembrane domain structure to more recent homology modelling based on recently crystallised bacterial members of the major facilitator superfamily (MFS). Substrate modelling has involved the proposal of a substrate binding template, to which all substrates must conform and from which the affinity of a substrate can be estimated relatively accurately, and identification of points of potential interaction of the substrate with the protein by developing a pharmacophore model of the substrates. Most recently, these two approaches have moved closer together, with the attempted docking of a substrate library onto a homology model of the human PepT1 protein. This article will review these two approaches in which computers have been applied to peptide transport and suggest how such computer modelling could affect drug design and delivery through PepT1.

This article provides an overview of the broad and increasingly varied selection of computational approaches available to find bioisosteric replacements for fragments of bioactive compounds. The rapidly increasing number and diversity of methods has provided medicinal chemists with a powerful range of commercial and academic tools to aid in the optimization of lead compound activity and ADMET properties for drug design. We discuss methods with fundamentally different philosophies, ranging from evaluation of similarity in a calculated property space to cheminformatics analysis of pharmaceutical compound databases. We also discuss the incorporation, within these methods, of a whole spectrum of experimental and calculated data to describe fragment chemistry and compound activity. Despite the growing sophistication of available techniques, there remains much scope for further development and especially for deeper validation of the efficacy of different approaches in what seems set to remain an expanding field.

Nuclear receptors (NRs) are ligand-inducible transcription factors that regulate a plethora of cell biological phenomena, thus orchestrating complex events like development, organ homeostasis, immune function, and reproduction. Due to their regulatory potential, NRs are major drug targets for a variety of diseases, including cancer and metabolic diseases, and had a major societal impact following the development of contraceptives and abortifacients. Not surprisingly in view of this medical and societal importance, a large amount of diverse NR ligands have been generated and the corresponding structural and functional analyses have provided a deep insight into the molecular basis of ligand action. What we have learnt is that ligands regulate, via allosteric conformational changes, the ability of NRs to interact with different sets of coregulators which in turn recruit enzymatically active complexes, the workhorses of the ligand-induced epigenetic and transcription-regulatory events. Thus, ligands essentially direct the communication of a given NR with its intracellular environment at the chromatin and extragenomic level to modulate gene programs directly at the chromatin level or via less well-understood extranuclear actions. Here we will review our current structural and mechanistic insight into the functionalities of subsets of retinoid and rexinoid ligands that act generically as antagonists but follow different mechanistic principles, resulting in "classical" or neutral antagonism, or inverse agonism. In addition, we describe the chemical features and guidelines for the synthesis of retinoids/rexinoids that exert specific functions and we provide protocols for a number of experimental approaches that are useful for studies of the agonistic and antagonistic features of NR ligands., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Fragment-based and de novo design strategies have been used in drug discovery for years. The methodologies for these strategies are typically discussed separately, yet the applications of these techniques overlap substantially. We present a review of various fragment-based discovery and de novo design protocols with an emphasis on successful applications in real-world drug discovery projects. Furthermore, we illustrate the strengths and weaknesses of the various approaches and discuss how one method can be used to complement another. We also discuss how the incorporation of experimental data as constraints in computational models can produce novel compounds that occupy unique areas in intellectual property (IP) space yet are biased toward the desired chemical property space. Finally, we present recent research results suggesting that computational tools applied to fragment-based discovery and de novo design can have a greater impact on the discovery process when coupled with the right experiments.

Cytochromes P450 (CYPs) are crucial targets when predicting the ADME properties (absorption, distribution, metabolism, and excretion) of drugs in development. Particularly, CYPs mediated drug-drug interactions are responsible for major failures in the drug design process. Accurate and robust screening filters are thus needed to predict interactions of potent compounds with CYPs as early as possible in the process. In recent years, more and more 3D structures of various CYP isoforms have been solved, opening the gate of accurate structure-based studies of interactions. Nevertheless, the ligand-based approach still remains popular. This success can be explained by the growing number of available data and the satisfying performances of existing machine learning (ML) methods. The aim of this contribution is to give an overview of the recent achievements in ML applications to CYP datasets. Particularly, popular methods such as support vector machine, decision trees, artificial neural networks, k-nearest neighbors, and partial least squares will be compared as well as the quality of the datasets and the descriptors used. Consensus of different methods will also be discussed. Often reaching 90% of accuracy, the models will be analyzed to highlight the key descriptors permitting the good prediction of CYPs binding.

The identification of new lead structures is a pivotal task in early drug discovery. Molecular de novo design of ligand structures has been successfully applied in various drug discovery projects. Still, the question of the scaffold hopping potential of drug design by adaptive evolutionary optimization has been left unanswered. It was unclear whether de novo design is actually able to leap away from given chemotypes ("activity islands"), allowing for rescaffolding of compounds. We have addressed these questions by scrutinizing different scoring functions of our de novo design software Flux for their ability to enable scaffold-hops for various target classes. We evaluated both the potential bioactivity and the scaffold diversity of de novo generated structures. For several target classes, known lead structures were reconstructed by the de novo algorithm ("lead-hopping"). We demonstrate that for one or multiple templates of a given chemotype, other chemotypes are reached during de novo compound generation, thus indicating successful scaffold-hops.