Rescuing drug discovery: in vivo systems pathology and systems pharmacology

Fragment-Based Drug Discovery: Small Fragments, Big Impact - Success Stories of Approved Oncology Therapeutics

Fragment-Based Drug Discovery (FBDD) has revolutionized drug discovery by overcoming the challenges of traditional methods like combinatorial chemistry and high-throughput screening (HTS). Leveraging small, low-molecular-weight fragments, FBDD achieves higher hit rates, reduced screening costs, and faster development timelines for clinically relevant drug candidates. This review explores FBDD's core principles, innovative methodologies, and its success in targeting diverse protein classes, including previously "undruggable" targets. Key advancements in fragment libraries, screening techniques, and computational tools are discussed, along with the efficient progression from fragment hits to clinical drugs. Notably, we highlight FDA-approved fragment-derived drugs, including capivasertib, which has increased the total number of fragment-based oncology drugs to seven. As FBDD continues to evolve, its potential to address unmet therapeutic needs and drive the discovery of groundbreaking treatments across various disease areas becomes increasingly evident.

Towards a comprehensive assessment of QSP models: what would it take?

Quantitative Systems Pharmacology (QSP) has emerged as a powerful ensemble of approaches aiming at developing integrated mathematical and computational models elucidating the complex interactions between pharmacology, physiology, and disease. As the field grows and matures its applications expand beyond the boundaries of research and development and slowly enter the decision making and regulatory arenas. However, widespread acceptance and eventual adoption of a new modeling approach requires assessment criteria and quantifiable metrics that establish credibility and increase confidence in model predictions. QSP aims to provide an integrated understanding of pathology in the context of therapeutic interventions. Because of its ambitious nature and the fact that QSP emerged in an uncoordinated manner as a result of activities distributed across organizations and academic institutions, high entropy characterizes the tools, methods, and computational methodologies and approaches used. The eventual acceptance of QSP model predictions as supporting material for an application to a regulatory agency will require that two key aspects are considered: (1) increase confidence in the QSP framework, which drives standardization and assessment; and (2) careful articulation of the expectations. Both rely heavily on our ability to rigorously and consistently assess QSP models. In this manuscript, we wish to discuss the meaning and purpose of such an assessment in the context of QSP model development and elaborate on the differentiating features of QSP that render such an endeavor challenging. We argue that QSP establishes a conceptual, integrative framework rather than a specific and well-defined computational methodology. QSP elicits the use of a wide variety of modeling and computational methodologies optimized with respect to specific applications and available data modalities, which exceed the data structures employed by chemometrics and PK/PD models. While the range of options fosters creativity and promises to substantially advance our ability to design pharmaceutical interventions rationally and optimally, our expectations of QSP models need to be clearly articulated and agreed on, with assessment emphasizing the scope of QSP studies rather than the methods used. Nevertheless, QSP should not be considered an independent approach, rather one of many in the broader continuum of computational models.

Genetically Encoded Detection of Biosynthetic Protease Inhibitors

Proteases are an important class of drug targets that continue to drive inhibitor discovery. These enzymes are prone to resistance mutations, yet their promise for treating viral diseases and other disorders continues to grow. This study develops a general approach for detecting microbially synthesized protease inhibitors and uses it to screen terpenoid pathways for inhibitory compounds. The detection scheme relies on a bacterial two-hybrid (B2H) system that links protease inactivation to the transcription of a swappable reporter gene. This system, which can accomodate multiple biochemical outputs (i.e., luminescence and antibiotic resistance), permitted the facile incorporation of four disease-relevant proteases. A B2H designed to detect the inactivation of the main protease of severe acute respiratory syndrome coronavirus 2 enabled the identification of a terpenoid inhibitor of modest potency. An analysis of multiple pathways that make this terpenoid, however, suggested that its production was necessary but not sufficient to confer a survival advantage in growth-coupled assays. This finding highlights an important challenge associated with the use of genetic selection to search for inhibitors─notably, the influence of pathway toxicity─and underlines the value of including multiple pathways with overlapping product profiles in pathway screens. This study provides a detailed experimental framework for using microbes to screen libraries of biosynthetic pathways for targeted protease inhibitors.

Survivin Splice Variant 2β Enhances Pancreatic Ductal Adenocarcinoma Resistance to Gemcitabine

Background

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease with poor prognosis, as it is difficult to predict or circumvent, and it develops chemoresistance quickly. One cellular mechanism associated with chemoresistance is alternative splicing dysfunction, a process through which nascent mRNA is spliced into different isoforms. Survivin (Baculoviral IAP Repeat-Containing Protein 5 (BIRC5)), a member of the inhibitor of apoptosis (IAP) protein family and a cell cycle-associated oncoprotein, is overexpressed in most cancers and undergoes alternative splicing (AS) to generate six different splicing isoforms.

Methods

To determine if survivin splice variants (SSV) could be involved in PDAC chemoresistance, a Gemcitabine (Gem) resistant (GR) cell line, MIA PaCa-2 GR, was created and assessed for its SSV levels and their potential association with GR. Cross-resistance was assessed in MIA-PaCa-2 GR cells to FIRINOX (5-fluorouracil (5-FU), irinotecan, and oxaliplatin). Once chemoresistance was confirmed, RT-qPCR was used to assess the expression of survivin splice variants (SSVs) in PDAC cell lines. To confirm the effect of SSVs on chemoresistance, we used siRNA to knockdown all SSVs or SSV 2β.

Results

The MIA PaCa-2 GR cell line was 40 times more resistant to Gem and revealed increased resistance to FIRINOX (5-fluorouracil (5-FU), irinotecan, and oxaliplatin); when compared to the parental MIA-PaCa-2 cells. RT-qPCR studies revealed an 8-fold relative expression increase in SSV 2β and a 2- to 8-fold increase in the other five SSVs in the GR cells. Knockdown of all SSV or SSV 2β only, using small inhibitory RNA (siRNA), sensitized the GR cells to Gem, indicating that these SSVs play a role in PDAC chemoresistance.

Conclusion

These findings provide evidence for the potential role of SSV 2β and other SSVs in innate and acquired PDAC chemoresistance. We also show that the expression of SSVs is not affected by the type of chemoresistance, therefore targeting survivin splice variants in combination with chemotherapy could benefit a wide range of patients.

Network Pharmacology Approach for Medicinal Plants: Review and Assessment

Natural products have played a critical role in medicine due to their ability to bind and modulate cellular targets involved in disease. Medicinal plants hold a variety of bioactive scaffolds for the treatment of multiple disorders. The less adverse effects, affordability, and easy accessibility highlight their potential in traditional remedies. Identifying pharmacological targets from active ingredients of medicinal plants has become a hot topic for biomedical research to generate innovative therapies. By developing an unprecedented opportunity for the systematic investigation of traditional medicines, network pharmacology is evolving as a systematic paradigm and becoming a frontier research field of drug discovery and development. The advancement of network pharmacology has opened up new avenues for understanding the complex bioactive components found in various medicinal plants. This study is attributed to a comprehensive summary of network pharmacology based on current research, highlighting various active ingredients, related techniques/tools/databases, and drug discovery and development applications. Moreover, this study would serve as a protocol for discovering novel compounds to explore the full range of biological potential of traditionally used plants. We have attempted to cover this vast topic in the review form. We hope it will serve as a significant pioneer for researchers working with medicinal plants by employing network pharmacology approaches.

Second-Generation JK-206 Targets the Oncogenic Signal Mediator RHOA in Gastric Cancer

Ras homologous A (RHOA), a signal mediator and a GTPase, is known to be associated with the progression of gastric cancer (GC), which is the fourth most common cause of death in the world. Previously, we designed pharmacologically optimized inhibitors against RHOA, including JK-136 and JK-139. Based on this previous work, we performed lead optimization and designed novel RHOA inhibitors for greater anti-GC potency. Two of these compounds, JK-206 and JK-312, could successfully inhibit the viability and migration of GC cell lines. Furthermore, using transcriptomic analysis of GC cells treated with JK-206, we revealed that the inhibition of RHOA might be associated with the inhibition of the mitogenic pathway. Therefore, JK-206 treatment for RHOA inhibition may be a new therapeutic strategy for regulating GC proliferation and migration.

Exposure-response modeling improves selection of radiation and radiosensitizer combinations

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

Systematic risk identification and assessment using a new risk map in pharmaceutical R&D

Delivering transformative therapies to patients while maintaining growth in the pharmaceutical industry requires an efficient use of research and development (R&D) resources and technologies to develop high-impact new molecular entities (NMEs). However, increasing global R&D competition in the pharmaceutical industry, growing impact of generics and biosimilars, more stringent regulatory requirements, as well as cost-constrained reimbursement frameworks challenge current business models of leading pharmaceutical companies. Big data-based analytics and artificial intelligence (AI) approaches have disrupted various industries and are having an increasing impact in the biopharmaceutical industry, with the promise to improve and accelerate biopharmaceutical R&D processes. Here, we systematically analyze, identify, assess, and categorize key risks across the drug discovery and development value chain using a new risk map approach, providing a comprehensive risk-reward analysis for pharmaceutical R&D.

A systems pharmacology approach based on oncogenic signalling pathways to determine the mechanisms of action of natural products in breast cancer from transcriptome data

BACKGROUND

Narrow spectrum of action through limited molecular targets and unforeseen drug-related toxicities have been the main reasons for drug failures at the phase I clinical trials in complex diseases. Most plant-derived compounds with medicinal values possess poly-pharmacologic properties with overall good tolerability, and, thus, are appropriate in the management of complex diseases, especially cancers. However, methodological limitations impede attempts to catalogue targeted processes and infer systemic mechanisms of action. While most of the current understanding of these compounds is based on reductive methods, it is increasingly becoming clear that holistic techniques, leveraging current improvements in omic data collection and bioinformatics methods, are better suited for elucidating their systemic effects. Thus, we developed and implemented an integrative systems biology pipeline to study these compounds and reveal their mechanism of actions on breast cancer cell lines.

METHODS

Transcriptome data from compound-treated breast cancer cell lines, representing triple negative (TN), luminal A (ER+) and HER2+ tumour types, were mapped on human protein interactome to construct targeted subnetworks. The subnetworks were analysed for enriched oncogenic signalling pathways. Pathway redundancy was reduced by constructing pathway-pathway interaction networks, and the sets of overlapping genes were subsequently used to infer pathway crosstalk. The resulting filtered pathways were mapped on oncogenesis processes to evaluate their anti-carcinogenic effectiveness, and thus putative mechanisms of action.

RESULTS

The signalling pathways regulated by Actein, Withaferin A, Indole-3-Carbinol and Compound Kushen, which are extensively researched compounds, were shown to be projected on a set of oncogenesis processes at the transcriptomic level in different breast cancer subtypes. The enrichment of well-known tumour driving genes indicate that these compounds indirectly dysregulate cancer driving pathways in the subnetworks.

CONCLUSION

The proposed framework infers the mechanisms of action of potential drug candidates from their enriched protein interaction subnetworks and oncogenic signalling pathways. It also provides a systematic approach for evaluating such compounds in polygenic complex diseases. In addition, the plant-based compounds used here show poly-pharmacologic mechanism of action by targeting subnetworks enriched with cancer driving genes. This network perspective supports the need for a systemic drug-target evaluation for lead compounds prior to efficacy experiments.

Machine Learning Prediction of Allosteric Drug Activity from Molecular Dynamics

Allosteric drugs have been attracting increasing interest over the past few years. In this context, it is common practice to use high-throughput screening for the discovery of non-natural allosteric drugs. While the discovery stage is supported by a growing amount of biological information and increasing computing power, major challenges still remain in selecting allosteric ligands and predicting their effect on the target protein's function. Indeed, allosteric compounds can act both as inhibitors and activators of biological responses. Computational approaches to the problem have focused on variations on the theme of molecular docking coupled to molecular dynamics with the aim of recovering information on the (long-range) modulation typical of allosteric proteins.

Drug Research Meets Network Science: Where Are We?

Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.

Model-Informed Drug Discovery and Development Strategy for the Rapid Development of Anti-Tuberculosis Drug Combinations

The increasing emergence of drug-resistant tuberculosis requires new effective and safe drug regimens. However, drug discovery and development are challenging, lengthy and costly. The framework of model-informed drug discovery and development (MID3) is proposed to be applied throughout the preclinical to clinical phases to provide an informative prediction of drug exposure and efficacy in humans in order to select novel anti-tuberculosis drug combinations. The MID3 includes pharmacokinetic-pharmacodynamic and quantitative systems pharmacology models, machine learning and artificial intelligence, which integrates all the available knowledge related to disease and the compounds. A translational in vitro-in vivo link throughout modeling and simulation is crucial to optimize the selection of regimens with the highest probability of receiving approval from regulatory authorities. In vitro-in vivo correlation (IVIVC) and physiologically-based pharmacokinetic modeling provide powerful tools to predict pharmacokinetic drug-drug interactions based on preclinical information. Mechanistic or semi-mechanistic pharmacokinetic-pharmacodynamic models have been successfully applied to predict the clinical exposure-response profile for anti-tuberculosis drugs using preclinical data. Potential pharmacodynamic drug-drug interactions can be predicted from in vitro data through IVIVC and pharmacokinetic-pharmacodynamic modeling accounting for translational factors. It is essential for academic and industrial drug developers to collaborate across disciplines to realize the huge potential of MID3.

Excitation states of metabolic networks predict dose-response fingerprinting and ligand pulse phase signalling

With a computational model of energy metabolism in an astrocyte, we show how a system of enzymes in a cascade can act as a functional unit of interdependent reactions, rather than merely a series of independent reactions. These systems may exist in multiple states, depending on the level of stimulation, and the effects of substrates at any point will depend on those states. Response trajectories of metabolites downstream from cAMP-stimulated glycogenolysis exhibit a host of non-linear dynamical response characteristics including hysteresis and response envelopes. Dose-dependent phase transitions predict a novel intracellular signalling mechanism and suggest a theoretical framework that could be relevant to single cell information processing, drug discovery or synthetic biology. Ligands may produce unique dose-response fingerprints depending on the state of the system, allowing selective output tuning. We conclude with the observation that state- and dose-dependent phase transitions, what we dub "ligand pulses" (LPs), may carry information and resemble action potentials (APs) generated from excitatory postsynaptic potentials. In our model, the relevant information from a cAMP-dependent glycolytic cascade in astrocytes could reflect the level of neuromodulatory input that signals an energy demand threshold. We propose that both APs and LPs represent specialized cases of molecular phase signalling with a common evolutionary root.

Predicting In Vivo Efficacy from In Vitro Data: Quantitative Systems Pharmacology Modeling for an Epigenetic Modifier Drug in Cancer

Reliably predicting in vivo efficacy from in vitro data would facilitate drug development by reducing animal usage and guiding drug dosing in human clinical trials. However, such prediction remains challenging. Here, we built a quantitative pharmacokinetic/pharmacodynamic (PK/PD) mathematical model capable of predicting in vivo efficacy in animal xenograft models of tumor growth while trained almost exclusively on in vitro cell culture data sets. We studied a chemical inhibitor of LSD1 (ORY‐1001), a lysine‐specific histone demethylase enzyme with epigenetic function, and drug‐induced regulation of target engagement, biomarker levels, and tumor cell growth across multiple doses administered in a pulsed and continuous fashion. A PK model of unbound plasma drug concentration was linked to the in vitro PD model, which enabled the prediction of in vivo tumor growth dynamics across a range of drug doses and regimens. Remarkably, only a change in a single parameter—the one controlling intrinsic cell/tumor growth in the absence of drug—was needed to scale the PD model from the in vitro to in vivo setting. These findings create a framework for using in vitro data to predict in vivo drug efficacy with clear benefits to reducing animal usage while enabling the collection of dense time course and dose response data in a highly controlled in vitro environment.

Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges

The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.

Novel Compound-Target Interactions Prediction for the Herbal Formula Hua-Yu-Qiang-Shen-Tong-Bi-Fang

Herbal formulae have a long history in clinical medicine in Asia. While the complexity of the formulae leads to the complex compound-target interactions and the resultant multi-target therapeutic effects, it is difficult to elucidate the molecular/therapeutic mechanism of action for the many formulae. For example, the Hua-Yu-Qiang-Shen-Tong-Bi-Fang (TBF), an herbal formula of Chinese medicine, has been used for treating rheumatoid arthritis. However, the target information of a great number of compounds from the TBF formula is missing. In this study, we predicted the targets of the compounds from the TBF formula via network analysis and in silico computing. Initially, the information of the phytochemicals contained in the plants of the herbal formula was collected, and subsequently computed to their corresponding fingerprints for the sake of structural similarity calculation. Then a compound structural similarity network infused with available target information was constructed. Five local similarity indices were used and compared for their performance on predicting the potential new targets of the compounds. Finally, the Preferential Attachment Index was selected for it having an area under curve (AUC) of 0.886, which outperforms the other four algorithms in predicting the compound-target interactions. This method could provide a promising direction for identifying the compound-target interactions of herbal formulae in silico.

Blood-Based Biomarkers of Quinpirole Pharmacology: Cluster-Based PK/PD and Metabolomics to Unravel the Underlying Dynamics in Rat Plasma and Brain

A key challenge in the development of central nervous system drugs is the availability of drug target specific blood-based biomarkers. As a new approach, we applied cluster-based pharmacokinetic/pharmacodynamic (PK/PD) analysis in brain extracellular fluid (brainECF ) and plasma simultaneously after 0, 0.17, and 0.86 mg/kg of the dopamine D2/3 agonist quinpirole (QP) in rats. We measured 76 biogenic amines in plasma and brainECF after single and 8-day administration, to be analyzed by cluster-based PK/PD analysis. Multiple concentration-effect relations were observed with potencies ranging from 0.001-383 nM. Many biomarker responses seem to distribute over the blood-brain barrier (BBB). Effects were observed for dopamine and glutamate signaling in brainECF , and branched-chain amino acid metabolism and immune signaling in plasma. Altogether, we showed for the first time how cluster-based PK/PD could describe a systems-response across plasma and brain, thereby identifying potential blood-based biomarkers. This concept is envisioned to provide an important connection between drug discovery and early drug development.

Fingerprints of CNS drug effects: a plasma neuroendocrine reflection of D2 receptor activation using multi-biomarker pharmacokinetic/pharmacodynamic modelling

BACKGROUND AND PURPOSE

Because biological systems behave as networks, multi-biomarker approaches increasingly replace single biomarker approaches in drug development. To improve the mechanistic insights into CNS drug effects, a plasma neuroendocrine fingerprint was identified using multi-biomarker pharmacokinetic/pharmacodynamic (PK/PD) modelling. Short- and long-term D2 receptor activation was evaluated using quinpirole as a paradigm compound.

EXPERIMENTAL APPROACH

Rats received 0, 0.17 or 0.86 mg·kg-1 of the D2 agonist quinpirole i.v. Quinpirole concentrations in plasma and brain extracellular fluid (brainECF ), as well as plasma concentrations of 13 hormones and neuropeptides, were measured. Experiments were performed at day 1 and repeated after 7-day s.c. drug administration. PK/PD modelling was applied to identify the in vivo concentration-effect relations and neuroendocrine dynamics.

KEY RESULTS

The quinpirole pharmacokinetics were adequately described by a two-compartment model with an unbound brainECF -to-plasma concentration ratio of 5. The release of adenocorticotropic hormone (ACTH), growth hormone, prolactin and thyroid-stimulating hormone (TSH) from the pituitary was influenced. Except for ACTH, D2 receptor expression levels on the pituitary hormone-releasing cells predicted the concentration-effect relationship differences. Baseline levels (ACTH, prolactin, TSH), hormone release (ACTH) and potency (TSH) changed with treatment duration.

CONCLUSIONS AND IMPLICATIONS

The integrated multi-biomarker PK/PD approach revealed a fingerprint reflecting D2 receptor activation. This forms the conceptual basis for in vivo evaluation of on- and off-target CNS drug effects. The effect of treatment duration is highly relevant given the long-term use of D2 agonists in clinical practice. Further development towards quantitative systems pharmacology models will eventually facilitate mechanistic drug development.

Model reduction in mathematical pharmacology : Integration, reduction and linking of PBPK and systems biology models

In this paper we present a framework for the reduction and linking of physiologically based pharmacokinetic (PBPK) models with models of systems biology to describe the effects of drug administration across multiple scales. To address the issue of model complexity, we propose the reduction of each type of model separately prior to being linked. We highlight the use of balanced truncation in reducing the linear components of PBPK models, whilst proper lumping is shown to be efficient in reducing typically nonlinear systems biology type models. The overall methodology is demonstrated via two example systems; a model of bacterial chemotactic signalling in Escherichia coli and a model of extracellular regulatory kinase activation mediated via the extracellular growth factor and nerve growth factor receptor pathways. Each system is tested under the simulated administration of three hypothetical compounds; a strong base, a weak base, and an acid, mirroring the parameterisation of pindolol, midazolam, and thiopental, respectively. Our method can produce up to an 80% decrease in simulation time, allowing substantial speed-up for computationally intensive applications including parameter fitting or agent based modelling. The approach provides a straightforward means to construct simplified Quantitative Systems Pharmacology models that still provide significant insight into the mechanisms of drug action. Such a framework can potentially bridge pre-clinical and clinical modelling - providing an intermediate level of model granularity between classical, empirical approaches and mechanistic systems describing the molecular scale.

Particle morphology: an important factor affecting drug delivery by nanocarriers into solid tumors

INTRODUCTION

Efficient delivery of drugs by nanoparticles deep into solid tumors is the precondition of valid cancer therapy. Despite profound understanding of the delivery of spherical nanoparticles into solid tumor attained, insufficient attention was paid to anisotropic particles. Actually, owing to their structural asymmetry, some non-spherical particles exhibit significant advantages over their spherical counterparts.

AREAS COVERED

This review will focus on particles with different shapes (discoidal particle, nanorod, filamentous particle, single-walled carbon nanotube) and the influence of their morphological characteristics (size, aspect ratio, rigidity) on the process of drug delivery to solid tumor in view of systemic circulation, transport from circulation system to tumor tissue, intratumoral transport and uptake by tumor cells, on the basis of introduction of challenges for drug delivery to solid tumor. In addition, the morphological characteristics will be briefly introduced to provide an understanding of anisotropic particle morphology.

EXPERT OPINION

Anisotropic particles exhibit desirable properties such as enhanced circulation time and efficient tumor penetration that could serve as an enlightenment in the exploitation of novel non-spherical nanocarriers to clinical therapy. Yet, current understanding of how anisotropic particles interact with organism is insufficient, which restricts the biomedical application of anisotropic particles. Further work is desired for the development of practical fabrication of anisotropic particles, quantitative analysis of particle morphology, as well as profound understanding of new targeting mechanism and intratumoral penetration of anisotropic particles.

Is systems pharmacology ready to impact upon therapy development? A study on the cholesterol biosynthesis pathway

Background and Purpose

An ever‐growing wealth of information on current drugs and their pharmacological effects is available from online databases. As our understanding of systems biology increases, we have the opportunity to predict, model and quantify how drug combinations can be introduced that outperform conventional single‐drug therapies. Here, we explore the feasibility of such systems pharmacology approaches with an analysis of the mevalonate branch of the cholesterol biosynthesis pathway.

Experimental Approach

Using open online resources, we assembled a computational model of the mevalonate pathway and compiled a set of inhibitors directed against targets in this pathway. We used computational optimization to identify combination and dose options that show not only maximal efficacy of inhibition on the cholesterol producing branch but also minimal impact on the geranylation branch, known to mediate the side effects of pharmaceutical treatment.

Key Results

We describe serious impediments to systems pharmacology studies arising from limitations in the data, incomplete coverage and inconsistent reporting. By curating a more complete dataset, we demonstrate the utility of computational optimization for identifying multi‐drug treatments with high efficacy and minimal off‐target effects.

Conclusion and Implications

We suggest solutions that facilitate systems pharmacology studies, based on the introduction of standards for data capture that increase the power of experimental data. We propose a systems pharmacology workflow for the refinement of data and the generation of future therapeutic hypotheses.

Leishmania and other intracellular pathogens: selectivity, drug distribution and PK-PD

New drugs and treatments for diseases caused by intracellular pathogens, such as leishmaniasis and the Leishmania species, have proved to be some of the most difficult to discover and develop. The focus of discovery research has been on the identification of potent and selective compounds that inhibit target enzymes (or other essential molecules) or are active against the causative pathogen in phenotypic in vitro assays. Although these discovery paradigms remain an essential part of the early stages of the drug R & D pathway, over the past two decades additional emphasis has been given to the challenges needed to ensure that the potential anti-infective drugs distribute to infected tissues, reach the target pathogen within the host cell and exert the appropriate pharmacodynamic effect at these sites. This review will focus on how these challenges are being met in relation to Leishmania and the leishmaniases with lessons learned from drug R & D for other intracellular pathogens.

Novel CNS drug discovery and development approach: model-based integration to predict neuro-pharmacokinetics and pharmacodynamics

INTRODUCTION

CNS drug development has been hampered by inadequate consideration of CNS pharmacokinetic (PK), pharmacodynamics (PD) and disease complexity (reductionist approach). Improvement is required via integrative model-based approaches. Areas covered: The authors summarize factors that have played a role in the high attrition rate of CNS compounds. Recent advances in CNS research and drug discovery are presented, especially with regard to assessment of relevant neuro-PK parameters. Suggestions for further improvements are also discussed. Expert opinion: Understanding time- and condition dependent interrelationships between neuro-PK and neuro-PD processes is key to predictions in different conditions. As a first screen, it is suggested to use in silico/in vitro derived molecular properties of candidate compounds and predict concentration-time profiles of compounds in multiple compartments of the human CNS, using time-course based physiology-based (PB) PK models. Then, for selected compounds, one can include in vitro drug-target binding kinetics to predict target occupancy (TO)-time profiles in humans. This will improve neuro-PD prediction. Furthermore, a pharmaco-omics approach is suggested, providing multilevel and paralleled data on systems processes from individuals in a systems-wide manner. Thus, clinical trials will be better informed, using fewer animals, while also, needing fewer individuals and samples per individual for proof of concept in humans.

Large-scale cross-species chemogenomic platform proposes a new drug discovery strategy of veterinary drug from herbal medicines

Veterinary Herbal Medicine (VHM) is a comprehensive, current, and informative discipline on the utilization of herbs in veterinary practice. Driven by chemistry but progressively directed by pharmacology and the clinical sciences, drug research has contributed more to address the needs for innovative veterinary medicine for curing animal diseases. However, research into veterinary medicine of vegetal origin in the pharmaceutical industry has reduced, owing to questions such as the short of compatibility of traditional natural-product extract libraries with high-throughput screening. Here, we present a cross-species chemogenomic screening platform to dissect the genetic basis of multifactorial diseases and to determine the most suitable points of attack for future veterinary medicines, thereby increasing the number of treatment options. First, based on critically examined pharmacology and text mining, we build a cross-species drug-likeness evaluation approach to screen the lead compounds in veterinary medicines. Second, a specific cross-species target prediction model is developed to infer drug-target connections, with the purpose of understanding how drugs work on the specific targets. Third, we focus on exploring the multiple targets interference effects of veterinary medicines by heterogeneous network convergence and modularization analysis. Finally, we manually integrate a disease pathway to test whether the cross-species chemogenomic platform could uncover the active mechanism of veterinary medicine, which is exemplified by a specific network module. We believe the proposed cross-species chemogenomic platform allows for the systematization of current and traditional knowledge of veterinary medicine and, importantly, for the application of this emerging body of knowledge to the development of new drugs for animal diseases.

Label-Free Dynamic Mass Redistribution and Bio-Impedance Methods for Drug Discovery

Label-free biosensors are increasingly employed in drug discovery. Cell-based biosensors provide valuable insights into the biological consequences of exposing cells and tissues to chemical agents and the underlying molecular mechanisms associated with these effects. Optical biosensors based on the detection of dynamic mass redistribution (DMR) and impedance biosensors using cellular dielectric spectroscopy (CDS) capture changes of the cytoskeleton of living cells in real time. Because signal transduction correlates with changes in cell morphology, DMR and CDS biosensors are exquisitely suited for recording integrated cell responses in an unbiased, yet pathway-specific manner without the use of labels that may interfere with cell function. Described in this unit are several experimental approaches utilizing optical label-free system capturing dynamic mass redistribution (DMR) in living cells (Epic System) and an impedance-based CDS technology (CellKey). In addition, potential pitfalls associated with these assays and alternative approaches for overcoming such technical challenges are discussed. © 2017 by John Wiley & Sons, Inc.

In Vivo Systems Response Profiling and Multivariate Classification of CNS Active Compounds: A Structured Tool for CNS Drug Discovery

This paper describes the application of in vivo systems response profiling in CNS drug discovery by a process referred to as the Integrative Screening Process. The biological response profile, treated as an array, is used as major outcome for selection of candidate drugs. Dose-response data, including ex vivo brain monoaminergic biomarkers and behavioral descriptors, are systematically collected and analyzed by principal component analysis (PCA) and partial least-squares (PLS) regression, yielding multivariate characterization across compounds. The approach is exemplified by assessing a new class of CNS active compounds, the dopidines, compared to other monoamine modulating compounds including antipsychotics, antidepressants, and procognitive agents. Dopidines display a distinct phenotypic profile which has prompted extensive further preclinical and clinical investigations. In summary, in vivo profiles of CNS compounds are mapped, based on dose response studies in the rat. Applying a systematic and standardized work-flow, a database of in vivo systems response profiles is compiled, enabling comparisons and classification. This creates a framework for translational mapping, a crucial component in CNS drug discovery.

Enhancing Drug Efficacy and Therapeutic Index through Cheminformatics-Based Selection of Small Molecule Binary Weapons That Improve Transporter-Mediated Targeting: A Cytotoxicity System Based on Gemcitabine

The transport of drug molecules is mainly determined by the distribution of influx and efflux transporters for which they are substrates. To enable tissue targeting, we sought to develop the idea that we might affect the transporter-mediated disposition of small-molecule drugs via the addition of a second small molecule that of itself had no inhibitory pharmacological effect but that influenced the expression of transporters for the primary drug. We refer to this as a “binary weapon” strategy. The experimental system tested the ability of a molecule that on its own had no cytotoxic effect to increase the toxicity of the nucleoside analog gemcitabine to Panc1 pancreatic cancer cells. An initial phenotypic screen of a 500-member polar drug (fragment) library yielded three “hits.” The structures of 20 of the other 2,000 members of this library suite had a Tanimoto similarity greater than 0.7 to those of the initial hits, and each was itself a hit (the cheminformatics thus providing for a massive enrichment). We chose the top six representatives for further study. They fell into three clusters whose members bore reasonable structural similarities to each other (two were in fact isomers), lending strength to the self-consistency of both our conceptual and experimental strategies. Existing literature had suggested that indole-3-carbinol might play a similar role to that of our fragments, but in our hands it was without effect; nor was it structurally similar to any of our hits. As there was no evidence that the fragments could affect toxicity directly, we looked for effects on transporter transcript levels. In our hands, only the ENT1-3 uptake and ABCC2,3,4,5, and 10 efflux transporters displayed measurable transcripts in Panc1 cultures, along with a ribonucleoside reductase RRM1 known to affect gemcitabine toxicity. Very strikingly, the addition of gemcitabine alone increased the expression of the transcript for ABCC2 (MRP2) by more than 12-fold, and that of RRM1 by more than fourfold, and each of the fragment “hits” served to reverse this. However, an inhibitor of ABCC2 was without significant effect, implying that RRM1 was possibly the more significant player. These effects were somewhat selective for Panc cells. It seems, therefore, that while the effects we measured were here mediated more by efflux than influx transporters, and potentially by other means, the binary weapon idea is hereby fully confirmed: it is indeed possible to find molecules that manipulate the expression of transporters that are involved in the bioactivity of a pharmaceutical drug. This opens up an entirely new area, that of chemical genomics-based drug targeting.

Integration of pharmacometabolomics with pharmacokinetics and pharmacodynamics: towards personalized drug therapy

Personalized medicine, in modern drug therapy, aims at a tailored drug treatment accounting for inter-individual variations in drug pharmacology to treat individuals effectively and safely. The inter-individual variability in drug response upon drug administration is caused by the interplay between drug pharmacology and the patients' (patho)physiological status. Individual variations in (patho)physiological status may result from genetic polymorphisms, environmental factors (including current/past treatments), demographic characteristics, and disease related factors. Identification and quantification of predictors of inter-individual variability in drug pharmacology is necessary to achieve personalized medicine. Here, we highlight the potential of pharmacometabolomics in prospectively informing on the inter-individual differences in drug pharmacology, including both pharmacokinetic (PK) and pharmacodynamic (PD) processes, and thereby guiding drug selection and drug dosing. This review focusses on the pharmacometabolomics studies that have additional value on top of the conventional covariates in predicting drug PK. Additionally, employing pharmacometabolomics to predict drug PD is highlighted, and we suggest not only considering the endogenous metabolites as static variables but to include also drug dose and temporal changes in drug concentration in these studies. Although there are many endogenous metabolite biomarkers identified to predict PK and more often to predict PD, validation of these biomarkers in terms of specificity, sensitivity, reproducibility and clinical relevance is highly important. Furthermore, the application of these identified biomarkers in routine clinical practice deserves notable attention to truly personalize drug treatment in the near future.

Application of multi-target phytotherapeutic concept in malaria drug discovery: a systems biology approach in biomarker identification

There is an urgent need for new anti-malaria drugs with broad therapeutic potential and novel mode of action, for effective treatment and to overcome emerging drug resistance. Plant-derived anti-malarials remain a significant source of bioactive molecules in this regard. The multicomponent formulation forms the basis of phytotherapy. Mechanistic reasons for the poly-pharmacological effects of plants constitute increased bioavailability, interference with cellular transport processes, activation of pro-drugs/deactivation of active compounds to inactive metabolites and action of synergistic partners at different points of the same signaling cascade. These effects are known as the multi-target concept. However, due to the intrinsic complexity of natural products-based drug discovery, there is need to rethink the approaches toward understanding their therapeutic effect. This review discusses the multi-target phytotherapeutic concept and its application in biomarker identification using the modified reverse pharmacology - systems biology approach. Considerations include the generation of a product library, high throughput screening (HTS) techniques for efficacy and interaction assessment, High Performance Liquid Chromatography (HPLC)-based anti-malarial profiling and animal pharmacology. This approach is an integrated interdisciplinary implementation of tailored technology platforms coupled to miniaturized biological assays, to track and characterize the multi-target bioactive components of botanicals as well as identify potential biomarkers. While preserving biodiversity, this will serve as a primary step towards the development of standardized phytomedicines, as well as facilitate lead discovery for chemical prioritization and downstream clinical development.

A Philosophical Framework for Integrating Systems Pharmacology Models Into Pharmacometrics

The framework for systems pharmacology style models does not naturally sit with the usual scientific dogma of parsimony and falsifiability based on deductive reasoning. This does not invalidate the importance or need for overarching models based on pharmacology to describe and understand complicated biological systems. However, it does require some consideration on how systems pharmacology fits into the overall scientific approach.

Revealing the Neuroendocrine Response After Remoxipride Treatment Using Multi-Biomarker Discovery and Quantifying It by PK/PD Modeling

To reveal unknown and potentially important mechanisms of drug action, multi-biomarker discovery approaches are increasingly used. Time-course relationships between drug action and multi-biomarker profiles, however, are typically missing, while such relationships will provide increased insight in the underlying body processes. The aim of this study was to investigate the effect of the dopamine D2 antagonist remoxipride on the neuroendocrine system. Different doses of remoxipride (0, 0.7, 5.2, or 14 mg/kg) were administered to rats by intravenous infusion. Serial brain extracellular fluid (brainECF) and plasma samples were collected and analyzed for remoxipride pharmacokinetics (PK). Plasma samples were analyzed for concentrations of the eight pituitary-related hormones as a function of time. A Mann-Whitney test was used to identify the responding hormones, which were further analyzed by pharmacokinetic/pharmacodynamic (PK/PD) modeling. A three-compartment PK model adequately described remoxipride PK in plasma and brainECF. Not only plasma PRL, but also adrenocorticotrophic hormone (ACTH) concentrations were increased, the latter especially at higher concentrations of remoxipride. Brain-derived neurotropic factor (BDNF), follicle stimulating hormone (FSH), growth hormone (GH), luteinizing hormone (LH), and thyroid stimulating hormones (TSH) did not respond to remoxipride at the tested doses, while oxytocin (OXT) measurements were below limit of quantification. Precursor pool models were linked to brainECF remoxipride PK by E_max drug effect models, which could accurately describe the PRL and ACTH responses. To conclude, this study shows how a multi-biomarker identification approach combined with PK/PD modeling can reveal and quantify a neuroendocrine multi-biomarker response for single drug action.

Methods to study the development, anatomy, and function of the zebrafish inner ear across the life course

The inner ear is a remarkably intricate structure able to detect sound, motion, and gravity. During development of the zebrafish embryo, the ear undergoes dynamic morphogenesis from a simple epithelial vesicle into a complex labyrinth, consisting of three semicircular canals and three otolithic sensory organs, each with an array of differentiated cell types. This microcosm of biology has led to advances in understanding molecular and cellular changes in epithelial patterning and morphogenesis, through to mechanisms of mechanosensory transduction and the origins of reflexive behavior. In this chapter, we describe different methods to study the zebrafish ear, including high-speed imaging of otic cilia, confocal microscopy, and light-sheet fluorescent microscopy. Many dyes, antibodies, and transgenic lines for labeling the ear are available, and we provide a comprehensive review of these resources. The developing ear is amenable to genetic, chemical, and physical manipulations, including injection and transplantation. Chemical modulation of developmental signaling pathways has paved the way for zebrafish to be widely used in drug discovery. We describe two chemical screens with relevance to the ear: a fluorescent-based screen for compounds that protect against ototoxicity, and an in situ-based screen for modulators of a signaling pathway involved in semicircular canal development. We also describe methods for dissection and imaging of the adult otic epithelia. We review both manual and automated methods to test the function of the inner ear and lateral line, defects in which can lead to altered locomotor behavior. Finally, we review a collection of zebrafish models that are generating new insights into human deafness and vestibular disorders.

New strategy for drug discovery by large-scale association analysis of molecular networks of different species

The development of modern omics technology has not significantly improved the efficiency of drug development. Rather precise and targeted drug discovery remains unsolved. Here a large-scale cross-species molecular network association (CSMNA) approach for targeted drug screening from natural sources is presented. The algorithm integrates molecular network omics data from humans and 267 plants and microbes, establishing the biological relationships between them and extracting evolutionarily convergent chemicals. This technique allows the researcher to assess targeted drugs for specific human diseases based on specific plant or microbe pathways. In a perspective validation, connections between the plant Halliwell-Asada (HA) cycle and the human Nrf2-ARE pathway were verified and the manner by which the HA cycle molecules act on the human Nrf2-ARE pathway as antioxidants was determined. This shows the potential applicability of this approach in drug discovery. The current method integrates disparate evolutionary species into chemico-biologically coherent circuits, suggesting a new cross-species omics analysis strategy for rational drug development.

BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology

BindingDB, www.bindingdb.org, is a publicly accessible database of experimental protein-small molecule interaction data. Its collection of over a million data entries derives primarily from scientific articles and, increasingly, US patents. BindingDB provides many ways to browse and search for data of interest, including an advanced search tool, which can cross searches of multiple query types, including text, chemical structure, protein sequence and numerical affinities. The PDB and PubMed provide links to data in BindingDB, and vice versa; and BindingDB provides links to pathway information, the ZINC catalog of available compounds, and other resources. The BindingDB website offers specialized tools that take advantage of its large data collection, including ones to generate hypotheses for the protein targets bound by a bioactive compound, and for the compounds bound by a new protein of known sequence; and virtual compound screening by maximal chemical similarity, binary kernel discrimination, and support vector machine methods. Specialized data sets are also available, such as binding data for hundreds of congeneric series of ligands, drawn from BindingDB and organized for use in validating drug design methods. BindingDB offers several forms of programmatic access, and comes with extensive background material and documentation. Here, we provide the first update of BindingDB since 2007, focusing on new and unique features and highlighting directions of importance to the field as a whole.

Metabonomics and systems biology

Systems biology represents an integrative research strategy that studies the interactions between DNA, mRNA, protein, and metabolite level in an organism, thereby including the interactions with the physical environment and other organisms. The application of metabonomics, or the quantitative study of metabolites in biological systems, in systems biology is currently an emerging area of research, which can contribute to the discovery of (disease) signatures, drug targeting and design, and the further elucidation of basic and more complex biochemical principles. This chapter covers the contribution of metabonomics in advancing our understanding in systems biology.

Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738

Understanding the therapeutic effect of drug dose and scheduling is critical to inform the design and implementation of clinical trials. The increasing complexity of both mono, and particularly combination therapies presents a substantial challenge in the clinical stages of drug development for oncology. Using a systems pharmacology approach, we have extended an existing PK-PD model of tumor growth with a mechanistic model of the cell cycle, enabling simulation of mono and combination treatment with the ATR inhibitor AZD6738 and ionizing radiation. Using AZD6738, we have developed multi-parametric cell based assays measuring DNA damage and cell cycle transition, providing quantitative data suitable for model calibration. Our in vitro calibrated cell cycle model is predictive of tumor growth observed in in vivo mouse xenograft studies. The model is being used for phase I clinical trial designs for AZD6738, with the aim of improving patient care through quantitative dose and scheduling prediction.

Multifunctional enveloped nanodevices (MENDs)

It is anticipated that nucleic acid medicines will be in widespread use in the future, since they have the potential to cure diseases based on molecular mechanisms at the level of gene expression. However, intelligent delivery systems are required to achieve nucleic acid therapy, since they can perform their function only when they reach the intracellular site of action. We have been developing a multifunctional envelope-type nanodevice abbreviated as MEND, which consists of functional nucleic acids as a core and lipid envelope, and can control not only biodistribution but also the intracellular trafficking of nucleic acids. In this chapter, we review the development and evolution of the MEND by providing several successful examples, including the R8-MEND, the KALA-MEND, the MITO-Porter, the YSK-MEND, and the PALM.

DT-Web: a web-based application for drug-target interaction and drug combination prediction through domain-tuned network-based inference

BACKGROUND

The identification of drug-target interactions (DTI) is a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Algorithms may aim to design new therapies based on a single approved drug or a combination of them. Recently, recommendation methods relying on network-based inference in connection with knowledge coming from the specific domain have been proposed.

DESCRIPTION

Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network. This technique combined with domain-specific knowledge expressing drugs and targets similarity is used to compute recommendations for each drug. Our web interface allows the users: (i) to browse all the predictions inferred by the algorithm; (ii) to upload their custom data on which they wish to obtain a prediction through a DT-Hybrid based pipeline; (iii) to help in the early stages of drug combinations, repositioning, substitution, or resistance studies by finding drugs that can act simultaneously on multiple targets in a multi-pathway environment. Our system is periodically synchronized with DrugBank and updated accordingly. The website is free, open to all users, and available at http://alpha.dmi.unict.it/dtweb/.

CONCLUSIONS

Our web interface allows users to search and visualize information on drugs and targets eventually providing their own data to compute a list of predictions. The user can visualize information about the characteristics of each drug, a list of predicted and validated targets, associated enzymes and transporters. A table containing key information and GO classification allows the users to perform their own analysis on our data. A special interface for data submission allows the execution of a pipeline, based on DT-Hybrid, predicting new targets with the corresponding p-values expressing the reliability of each group of predictions. Finally, It is also possible to specify a list of genes tracking down all the drugs that may have an indirect influence on them based on a multi-drug, multi-target, multi-pathway analysis, which aims to discover drugs for future follow-up studies.