Recent Advances in Physicochemical and ADMET Profiling in Drug Discovery

The drastic increase in the cost for discovering and developing a new drug along with the high attrition rate of development candidates led to shifting drug‐discovery strategy to parallel assessment of comprehensive drug physicochemical, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties alongside efficacy. With the proposal of a profiling paradigm and utilization of integrated risk assessment, one can exponentially enhance the predictive power of in vitro tools by taking into consideration the interplay among profiling parameters. In particular, this article will review recent advances in accurate assessment of solubility and other physicochemical parameters. The proper interpretation of these experimental data is crucial for rapid and meaningful risk assessment and rational optimization of drug candidates in drug discovery. The impact of these tools on assisting drug‐discovery teams in establishing in vitro–in vivo correlation (IVIVC) as well as structure–property relationship (SPR) will be presented.     

Human hepatocytes: isolation, cryopreservation and applications in drug development

The recent developments in the isolation, culturing, and cryopreservation of human hepatocytes, and the application of the cells in drug development are reviewed. Recent advances include the improvement of cryopreservation procedures to allow cell attachment, thereby extending the use of the cells to assays that requires prolong culturing such as enzyme induction studies. Applications of human hepatocytes in drug development include the evaluation of metabolic stability, metabolite profiling and identification, drug-drug interaction potential, and hepatotoxic potential. The use of intact human hepatocytes, because of the complete, undisrupted metabolic pathways and cofactors, allows the development of data more relevant to humans in vivo than tissue fractions such as human liver microsomes. Incorporation of key in vivo factors with the intact hepatocytes in vitro may help predictive human in vivo drug properties. For instance, evaluation of drug metabolism and drug-drug interactions with intact human hepatocytes in 100% human serum may eliminate the need to determine in vivo intracellular concentrations for the extrapolation of in vitro data to in vivo. Co-culturing of hepatocytes and nonhepatic primary cells from other organs in the integrated discrete multiple organ co-culture (IdMOC) may allow the evaluation of multiple organ interactions in drug metabolism and drug toxicity. In conclusion, human hepatocytes represent a critical experimental model for drug development, allowing early evaluation of human drug properties to guide the design and selection of drug candidates with a high probability of clinical success.     

Microengineered systems with iPSC-derived cardiac and hepatic cells to evaluate drug adverse effects

Hepatic and cardiac drug adverse effects are among the leading causes of attrition in drug development programs, in part due to predictive failures of current animal or in vitro models. Hepatocytes and cardiomyocytes differentiated from human induced pluripotent stem cells (iPSCs) hold promise for predicting clinical drug effects, given their human-specific properties and their ability to harbor genetically determined characteristics that underlie inter-individual variations in drug response. Currently, the fetal-like properties and heterogeneity of hepatocytes and cardiomyocytes differentiated from iPSCs make them physiologically different from their counterparts isolated from primary tissues and limit their use for predicting clinical drug effects. To address this hurdle, there have been ongoing advances in differentiation and maturation protocols to improve the quality and use of iPSC-differentiated lineages. Among these are in vitro hepatic and cardiac cellular microsystems that can further enhance the physiology of cultured cells, can be used to better predict drug adverse effects, and investigate drug metabolism, pharmacokinetics, and pharmacodynamics to facilitate successful drug development. In this article, we discuss how cellular microsystems can establish microenvironments for these applications and propose how they could be used for potentially controlling the differentiation of hepatocytes or cardiomyocytes. The physiological relevance of cells is enhanced in cellular microsystems by simulating properties of tissue microenvironments, such as structural dimensionality, media flow, microfluidic control of media composition, and co-cultures with interacting cell types. Recent studies demonstrated that these properties also affect iPSC differentiations and we further elaborate on how they could control differentiation efficiency in microengineered devices. In summary, we describe recent advances in the field of cellular microsystems that can control the differentiation and maturation of hepatocytes and cardiomyocytes for drug evaluation. We also propose how future research with iPSCs within engineered microenvironments could enable their differentiation for scalable evaluations of drug effects.     

Protein therapeutics: promises and challenges for the 21st century.

Recent advances in massively parallel experimental and computational technologies are leading to radically new approaches to the early phases of the drug production pipeline. The revolution in DNA microarray technologies and the imminent emergence of its analogue for proteins, along with machine learning algorithms, promise rapid acceleration in the identification of potential drug targets, and in high-throughput screens for subpopulation-specific toxicity. Similarly, advances in structural genomics in conjunction with in vitro and in silico evolutionary methods will rapidly accelerate the number of lead drug candidates and substantially augment their target specificity. Taken collectively, these advances will usher in an era of predictive medicine, which will move medical practice from reactive therapy after disease onset, to proactive prevention.     

用于肿瘤治疗的免疫检查点抑制剂相关预测生物标志物的研究进展

恶性肿瘤是严重威胁人类健康和社会发展的疾病。传统的肿瘤治疗方法如手术、放疗、化疗和靶向治疗等不能完全满足临床治疗的需求,新兴的免疫治疗成为了肿瘤治疗领域的研究热点。免疫检查点抑制剂(immune checkpoint inhibitors,ICIs)作为一种肿瘤免疫治疗方法,已获批用于治疗多种肿瘤,如肺癌、肝癌、胃癌和结直肠癌等。然而,ICIs在临床使用过程中,只有少数患者会出现持久反应,一些患者还会出现耐药和不良反应。因此,预测生物标志物的鉴定和开发对提高ICIs的治疗效果至关重要。肿瘤ICIs预测生物标志物主要包括肿瘤生物标志物、肿瘤微环境生物标志物、循环相关生物标志物、宿主环境生物标志物以及组合生物标志物等,对患者筛查、个体化治疗和预后评估具有重要意义。本文就肿瘤ICIs治疗预测生物标志物的前沿进展作一综述。     

Towards machine learning for hydrogel drug delivery systems

Hydrogel drug delivery system development is complex and laborious, and machine learning (ML) techniques hold great promise in accelerating the process. We highlight recent advances and strategies for data collection and ML, and we discuss the potential for and barriers to the broader use of ML for hydrogel drug delivery systems.     

An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients’ tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.     

Prediction in the face of uncertainty: a Monte Carlo-based approach for systems biology of cancer treatment

Cancer is known to be a complex disease and its therapy is difficult. Much information is available on molecules and pathways involved in cancer onset and progression and this data provides a valuable resource for the development of predictive computer models that can help to identify new potential drug targets or to improve therapies. Modeling cancer treatment has to take into account many cellular pathways usually leading to the construction of large mathematical models. The development of such models is complicated by the fact that relevant parameters are either completely unknown, or can at best be measured under highly artificial conditions. Here we propose an approach for constructing predictive models of such complex biological networks in the absence of accurate knowledge on parameter values, and apply this strategy to predict the effects of perturbations induced by anti-cancer drug target inhibitions on an epidermal growth factor (EGF) signaling network. The strategy is based on a Monte Carlo approach, in which the kinetic parameters are repeatedly sampled from specific probability distributions and used for multiple parallel simulations. Simulation results from different forms of the model (e.g., a model that expresses a certain mutation or mutation pattern or the treatment by a certain drug or drug combination) can be compared with the unperturbed control model and used for the prediction of the perturbation effects. This framework opens the way to experiment with complex biological networks in the computer, likely to save costs in drug development and to improve patient therapy.     

Nanomaterials in controlled drug release

In past years with the advances of chemistry and material sciences, the development of nanotechnology brought generations of nanomaterials with specific biomedical properties. These include the nanoparticle-based drug delivery, nanosized drugs, and nanomaterials for tissue engineering. The present article focuses on the use of nanomaterials in controlled drug release. The applications of nanomaterials with nano-enabled drug release characteristics brought many benefits when compared to the traditional (bulk) materials. We discuss the current advances and propose some future directions for the technology development.     

Past and present concepts in flow cytometry: a European perspective

The development of flow cytometric instrumentation, methods and research concepts in Europe has been a continuous driving force for the general scientific advancement in this area over the years. This review addresses early European concepts of continuing interest with regard to instrumentation, data analysis, clinical and eperimental DNA analysis, cell function and microbiology at their worldwide first appearence while flow cytometric immunology and immunophenotyping will be covered separately. Flow cytometry represents an efficient approach to the enormous complexity of molecular cell architecture and cell function by the analysis of apparent molecular cell phenotypes in heterogeneous cell samples. The present merger of flow and image cytometry into the method independent cytomics discipline increases the potential of cell analysis very significantly. It opens the way for predictive medicine as well as for predictive cytopathology and predictive cytology in everyday clinical and medical practice. Current progress is driven by joint advances in molecular fluorescence technologies and instrument development. This complements the analysis of genome sequence information in an efficient way.     

Chemical proteomics and its impact on the drug discovery process

Despite the rapid growth of postgenomic data and fast-paced technology advancement, drug discovery is still a lengthy and difficult process. More effective drug design requires a better understanding of the interaction between drug candidates and their targets/off-targets in various situations. The ability of chemical proteomics to integrate a multiplicity of disciplines enables the direct analysis of protein activities on a proteome-wide scale, which has enormous potential to facilitate drug target elucidation and lead drug verification. Over recent years, chemical proteomics has experienced rapid growth and provided a valuable method for drug target identification and inhibitor discovery. This review introduces basic concepts and technologies of different popular chemical proteomic approaches. It also covers the essential features and recent advances of each approach while underscoring their potentials in drug discovery and development.     

High-throughput protein crystallization

The combinatorial chemistry industry has made major advances in the handling and mixing of small volumes, and in the development of robust liquid-handling systems. In addition, developments have been made in the area of material handling for the high-throughput drug screening and combinatorial chemistry fields. Lastly, improvements in beamline optics at synchrotron sources have enabled the use of flash-frozen micron-sized (10-50 microm) crystals. The combination of these and other recent advances will make high-throughput protein crystallography possible. Further advances in high-throughput methods of protein crystallography will require application of the above developments and the accumulation of success/failure data in a more systematic manner. Major changes in crystallography technology will emerge based on the data collected by first-generation high-throughput systems.     

Drug discovery for parasitic diseases: powered by technology,enabled by pharmacology,informed by clinical science

While prevention is a bedrock of public health, innovative therapeutics are needed to complement the armamentarium of interventions required to achieve disease control and elimination targets for neglected diseases. Extraordinary advances in drug discovery technologies have occurred over the past decades, along with accumulation of scientific knowledge and experience in pharmacological and clinical sciences that are transforming many aspects of drug R&D across disciplines. We reflect on how these advances have propelled drug discovery for parasitic infections, focusing on malaria, kinetoplastid diseases, and cryptosporidiosis. We also discuss challenges and research priorities to accelerate discovery and development of urgently needed novel antiparasitic drugs.     

High throughput and quantitative enzymology in the genomic era

Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.     

The emerging role of the physician in genetic counselling and testing for heritable breast,ovarian and colon cancer.

As a genetic testing for susceptibility to breast, ovarian and colon cancer becomes more readily available, physicians are faced with an increasing demand for information about inherited cancer risk. Because advances in treatment have not kept pace with advances in predictive testing, the provision of genetic counselling and testing marks a departure from the traditional role of the physician. A systematic framework is needed within which the physician''s emerging role in predictive testing for heritable cancer can be delineated. The development of such a framework will require collaboration among professionals in a range of scientific disciplines, as well as the suspension of traditional assumptions about the physicians role.     

Modeling opportunities in comparative oncology for drug development

Successful development of novel cancer drugs depends on well-reasoned scientific drug discovery, rigorous preclinical development, and carefully conceived clinical trials. Failure in any of these steps contributes to poor rates of approval for new drugs to treat cancer. As technological and scientific advances have opened the door to a variety of novel approaches to cancer drug discovery and development, preclinical models that can answer questions about the activity and safety of novel therapies are increasingly necessary. The advance of a drug to clinical trials based on information from preclinical models presupposes that the models convey informative data for future use in human patients with cancer. The study of novel cancer drugs using in vitro models is highly controllable, reproducible, relatively inexpensive, and linked to high throughput. However, these models fail to reproduce many of the complex features of human cancer. Mouse models address some of these limitations but have important biological differences from human cancer. The integration of studies using pet dogs with spontaneously occurring tumors as models in the development path can answer questions not adequately addressed in conventional models and is therefore gaining attention and interest in drug development communities. The study of novel cancer drugs in dogs with naturally occurring tumors allows drug assessment in a cancer that shares many fundamental features with the human cancer condition, and thus provides an opportunity to answer questions that inform the cancer drug development path in ways not possible in more conventional models.     

Biomarkers of cell death applicable to early clinical trials

The development of biomarkers of cell death to reflect tumor biology and drug-induced response has garnered interest with the development of several classes of drugs aimed at decreasing the cellular threshold for apoptosis and exploiting pre-existing oncogenic stresses. These novel anticancer drugs, directly targeted to the apoptosis regulatory machinery and aimed at abrogating survival signaling pathways, are entering early clinical trials provoking the question of how to monitor their impact on cancer patients. The parallel development of drugs with predictive biomarkers and their incorporation into early clinical trials are anticipated to support the pharmacological audit trail, to speed the development and reduce the attrition rate of novel drugs whose objective is to provoke tumor cell death. Tumor biopsies are an ideal matrix to measure apoptosis, but surrogate less invasive biomarkers such as blood samples and functional imaging are less challenging to acquire clinically. Archetypal and exploratory examples illustrating the importance of biomarkers to drug development are given. This review explores the substantive challenges associated with the validation, deployment, interpretation and utility of biomarkers of cell death and reviews recent advances in their incorporation in preclinical and early clinical trial contexts.     

Exploiting naturally occurring DNA variation and molecular profiling data to dissect disease and drug response traits

Identifying the key drivers of common human diseases and associated signaling pathways remains one of the primary objectives in the biomedical and life sciences. In this respect, common inbred strains of mice have played a crucial role, and recent advances in the development of genomics and bioinformatics tools have significantly enhanced their utility for this purpose. These advances have enabled a more holistic, network-oriented view of biological systems that facilitates elucidation of the underlying causes of disease and the best ways to target them. Success in reconstructing gene networks underlying disease traits (or other complex traits like drug response) and identifying the key drivers of these traits now largely rests on integrative approaches that combine data from multiple different sources. Such integrative genomics approaches that take into account genotypic, molecular profiling and clinical data in segregating mouse populations have recently been developed. Key to this integration has been the development and application of sophisticated algorithms to mine the diversity of data.     

Genomic selection prediction models comparing sequence capture and SNP array genotyping methods

The successful application of genomic selection (GS) approaches is dependent on genetic makers derived from high-throughput and low-cost genotyping methods. Recent GS studies in trees have predominantly relied on SNP arrays as the source of genotyping, though this technology has a high entry cost. The recent development of alternative genotyping platforms, tailored to specific species and with low entry cost, has become possible due to advances in next-generation sequencing and genome complexity reduction methods such as sequence capture. However, the performance of these new platforms in GS models has not yet been evaluated, or compared to models developed from SNP arrays. Here, we evaluate the impact of these genotyping technologies on the development of GS prediction models for a Eucalyptus breeding population composed of 739 trees phenotyped for 13 wood quality and growth traits. Genotyping data obtained with both methods were compared for linkage disequilibrium, minor allele frequency, and missing data. Phenotypic prediction methods RR-BLUP and BayesB were employed, while predictive ability using cross validation was used to evaluate the performance of GS models derived from the different genotyping platforms. Differences in linkage disequilibrium patterns, minor allele frequency, missing data, and marker distribution were detected between sequence capture and SNP arrays. However, RR-BLUP and BayesB GS models resulted in similar predictive abilities. These results demonstrate that both genotyping methods are equivalent for genomic prediction of the traits evaluated. Sequence capture offers an alternative for species where SNP arrays are not available, or for when the initial development cost is too high.