Drug Attrition Rates Research Articles

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.

The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.

Abstract 6907: Micro-implantation of patient tumor samples into the avian embryo, a fast and reliable alternative in vivo technology for preclinical studies on melanoma and follicular lymphoma

Abstract Despite recent efforts to develop reliable and predictive patient-derived cancer models, drug attrition rates in oncology remain high. Moreover, conventional models such as patient-derived xenograft (PDX) mouse models face major cost and time limitations, not supporting real-time clinical decision making. To overcome such limitation, ONCOFACTORY developed a proprietary PDX platform enabling the engraftment of small size tumor biopsy samples within targeted tissue of the avian embryo at early stage of development, to conduct preclinical proof of efficacy studies and provide PDX clinical trial solution helping with patient’s selection prior to clinical phase II/III. On one hand, we performed prospective co-clinical trial aiming to evaluate the utility of the AVI-PDXTM model in predicting the response to standard therapy, consisting of BRAF and MEK inhibitors, for patient with metastatic melanoma harboring BRAF mutations. Skin biopsy samples were implanted into specific tissue driving tumor formation under the skin of avian embryos. On the other hand, we conducted a retrospective cohort study with 20 follicular lymphoma (FL) patient samples divided into “partial” and “complete” responders to the standard immuno-chemotherapy RCHOP regimen to establish FL AVI-PDXTM, and examine sensitivity to RCHOP in ovo. Twenty-four hours post-implantation, avian melanoma and FL replicas were respectively exposed to BRAFi/MEKi or RCHOP and their vehicles, over 24h. Then, embryos were harvested, and tumor response was examined by measuring tumor volume using light sheet microscopy. Alongside, in independent series of avian embryos implanted with FL samples, tumors were harvested for single-cell transcriptomic analyses to explore mechanism of response to RCHOP. For melanoma, we provide the proof of concept that the AVI-PDXTM reliably predict sensitivity or resistance to BRAF and MEK inhibitors within days, matching patient clinical outcome observed in clinic. For FL, we showed that the FL-AVI-PDXTM efficiently captures clinical response to RCHOP in a heterogenous cohort of patients, enabling to discriminate partial and complete responder within short-time frame. Next, we identified a robust genetic signature reflecting exposure of tumoral cells to RCHOP and showed that drug-mediated targeting of one of these genes with a chemotherapy resulted in strong potentiation of RCHOP. Thus, we provide evidence of the high predictive power of the AVI-PDXTM models allowing fast and reproducible generation of tumors replicas from small size patient tumor samples. This places the AVI-PDXTM as a valuable tool for clinical research to rationalize patient inclusion in clinical trials but also for the design of personalized medicine assays. Citation Format: Clelia Costechareyre, Loraine Jarrosson, Marjorie Lacourrege, Romain Teinturier, Julie Caramel, Manon Zala, Stéphane Dalle, Pierre Sujobert, Céline Delloye-Bourgeois, Frédéric Berget, Valérie Castellani. Micro-implantation of patient tumor samples into the avian embryo, a fast and reliable alternative in vivo technology for preclinical studies on melanoma and follicular lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6907.

Microglial roles in Alzheimer's disease: An agent-based model to elucidate microglial spatiotemporal response to beta-amyloid.

Alzheimer's disease (AD) is characterized by beta-amyloid (Aβ) plaques in the brain and widespread neuronal damage. Because of the high drug attrition rates in AD, there is increased interest in characterizing neuroimmune responses to Aβ plaques. In response to AD pathology, microglia are innate phagocytotic immune cells that transition into a neuroprotective state and form barriers around plaques. We seek to understand the role of microglia in modifying Aβ dynamics and barrier formation. To quantify the influence of individual microglia behaviors (activation, chemotaxis, phagocytosis, and proliferation) on plaque size and barrier coverage, we developed an agent-based model to characterize the spatiotemporal interactions between microglia and Aβ. Our model qualitatively reproduces mouse data trends where the fraction of microglia coverage decreases as plaques become larger. In our model, the time to microglial arrival at the plaque boundary is significantly negatively correlated (p < 0.0001) with plaque size, indicating the importance of the time to microglial activation for regulating plaque size. In addition, in silico behavioral knockout simulations show that phagocytosis knockouts have the strongest impact on plaque size, but modest impacts on microglial coverage and activation. In contrast, the chemotaxis knockouts had a strong impact on microglial coverage with a more modest impact on plaque volume and microglial activation. These simulations suggest that phagocytosis, chemotaxis, and replication of activated microglia have complex impacts on plaque volume and coverage, whereas microglial activation remains fairly robust to perturbations of these functions. Thus, our work provides insights into the potential and limitations of targeting microglial activation as a pharmacological strategy for the treatment of AD.

Human liver organoid models for assessment of drug toxicity at the preclinical stage.

The hepatotoxicity of drugs is one of the leading causes of drug withdrawal from the pharmaceutical market and high drug attrition rates. Currently, the commonly used hepatocyte models include conventional hepatic cell lines and animal models, which cannot mimic human drug-induced liver injury (DILI) due to poorly defined dose-response relationships and/or lack of human-specific mechanisms of toxicity. In comparison to 2D culture systems from different cell sources such as primary human hepatocytes and hepatomas,, 3D organoids derived from an inducible pluripotent stem cell (iPSC) or adult stem cells are promising accurate models to mimic organ behavior with a higher level of complexity and functionality owing to their ability to self-renewal. Meanwhile, the heterogeneous cell composition of the organoids enables metabolic and functional zonation of hepatic lobule important in drug detoxification and has the ability to mimic idiosyncratic DILI as well. Organoids having higher drug-metabolizing enzyme capacities can culture long-term and be combined with microfluidic-based technologies such as organ-on-chips for a more precise representation of human susceptibility to drug response in a high-throughput manner. However, there are numerous limitations to be considered about this technology, such as enough maturation, differences between protocols and high cost. Herein, we first reviewed the current preclinical DILI assessment tools and looked at the organoid technology with respect to in vitro detoxification capacities. Then we discussed the clinically applicable DILI assessment markers and the importance of liver zonation in the next generation organoid-based DILI models.

Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humans.

Poor translation between invitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients' life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials. Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects. To evaluate the discrepancy to predict drug approval (1404 approved and 1070 unapproved drugs), we analysed CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects on cells and humans, respectively. To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems. Drug approvals in clinical trials were correlated with the cells/humans discrepancy in gene perturbation effects. Genes tolerant to perturbation effects on cells but intolerant to those on humans were associated with failed drug targets. Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects. Motivated by previous studies assessing drug safety through chemical properties, we improved drug approval prediction by integrating chemical information with the cells/humans discrepancy. The cells/humans discrepancy in gene perturbation effects facilitates drug approval prediction and explains drug safety failures in clinical trials. S.K. received grants from the Korean National Research Foundation (2021R1A2B5B01001903 and 2020R1A6A1A03047902) and IITP (2019-0-01906, Artificial Intelligence Graduate School Program, POSTECH).

Time to Change: A Systems Pharmacology Approach to Disentangle Mechanisms of Drug-Induced Mitochondrial Toxicity.

An increasing number of commonly prescribed drugs are known to interfere with mitochondrial function, which is associated with almost half of all Food and Drug Administration black box warnings, a variety of drug withdrawals, and attrition of drug candidates. This can mainly be attributed to a historic lack of sensitive and specific assays to identify the mechanisms underlying mitochondrial toxicity during drug development. In the last decade, a better understanding of drug-induced mitochondrial dysfunction has been achieved by network-based and structure-based systems pharmacological approaches. Here, we propose the implementation of a tiered systems pharmacology approach to detect adverse mitochondrial drug effects during preclinical drug development, which is based on a toolset developed to study inherited mitochondrial disease. This includes phenotypic characterization, profiling of key metabolic alterations, mechanistic studies, and functional in vitro and in vivo studies. Combined with binding pocket similarity comparisons and bottom-up as well as top-down metabolic network modeling, this tiered approach enables identification of mechanisms underlying drug-induced mitochondrial dysfunction. After validation of these off-target mechanisms, drug candidates can be adjusted to minimize mitochondrial activity. Implementing such a tiered systems pharmacology approach could lead to a more efficient drug development trajectory due to lower drug attrition rates and ultimately contribute to the development of safer drugs. SIGNIFICANCE STATEMENT: Many commonly prescribed drugs adversely affect mitochondrial function, which can be detected using phenotypic assays. However, these methods provide only limited insight into the underlying mechanisms. In recent years, a better understanding of drug-induced mitochondrial dysfunction has been achieved by network-based and structure-based system pharmacological approaches. Their implementation in preclinical drug development could reduce the number of drug failures, contributing to safer drug design.

Human-Induced Pluripotent Stem Cell-Based Differentiation of Cardiomyocyte Subtypes for Drug Discovery and Cell Therapy.

Drug attrition rates have increased over the past few years, accompanied with growing costs for the pharmaceutical industry and consumers. Lack of in vitro models connecting the results of toxicity screening assays with clinical outcomes accounts for this high attrition rate. The emergence of cardiomyocytes derived from human pluripotent stem cells provides an amenable source of cells for disease modeling, drug discovery, and cardiotoxicity screening. Functionally similar to to embryonic stem cells, but with fewer ethical concerns, induced pluripotent stem cells (iPSCs) can recapitulate patient-specific genetic backgrounds, which would be a huge revolution for personalized medicine. The generated iPSC-derived cardiomyocytes (iPSC-CMs) represent different subtypes including ventricular-, atrial-, and nodal-like cardiomyocytes. Purifying these subtypes for chamber-specific drug screening presents opportunities and challenges. In this chapter, we discuss the strategies for the purification of iPSC-CMs, the use of iPSC-CMs for drug discovery and cardiotoxicity test, and the current limitations of iPSC-CMs that should be overcome for wider and more precise cardiovascular applications.

Hallmarks of neurodegenerative disease: A systems pharmacology perspective.

Age-related central neurodegenerative diseases, such as Alzheimer's and Parkinson's disease, are a rising public health concern and have been plagued by repeated drug development failures. The complex nature and poor mechanistic understanding of the etiology of neurodegenerative diseases has hindered the discovery and development of effective disease-modifying therapeutics. Quantitative systems pharmacology models of neurodegeneration diseases may be useful tools to enhance the understanding of pharmacological intervention strategies and to reduce drug attrition rates. Due to the similarities in pathophysiological mechanisms across neurodegenerative diseases, especially at the cellular and molecular levels, we envision the possibility of structural components that are conserved across models of neurodegenerative diseases. Conserved structural submodels can be viewed as building blocks that are pieced together alongside unique disease components to construct quantitative systems pharmacology (QSP) models of neurodegenerative diseases. Model parameterization would likely be different between the different types of neurodegenerative diseases as well as individual patients. Formulating our mechanistic understanding of neurodegenerative pathophysiology as a mathematical model could aid in the identification and prioritization of drug targets and combinatorial treatment strategies, evaluate the role of patient characteristics on disease progression and therapeutic response, and serve as a central repository of knowledge. Here, we provide a background on neurodegenerative diseases, highlight hallmarks of neurodegeneration, and summarize previous QSP models of neurodegenerative diseases.

Bioprinting and its Use in Tumor-On-A-Chip Technology for Cancer Drug Screening: A Review

The rising global incidence of cancer and high attrition rates of anticancer drugs make it imperative to design novel screening platforms to increase the success rate of chemotherapeutic agents. Advances in cell culture models from two-dimensional to three-dimensional platforms, along with microfluidics, have resulted in the creation of tumor-on-a-chip technology, which enables high-throughput molecular screening and helps to simulate the dynamic tumor microenvironment. Furthermore, advancements in bioprinting have allowed the structural and physiological aspects of the tumor to be recreated accurately and help to mimic cell-cell interactions and cell-extracellular matrix. This paper provides a comprehensive review of three-dimensional bioprinting to fabricate a tumor-on-a-chip platform to advance the discovery and screening of anticancer agents and provides a perspective on the challenges and future directions associated with the adoption of this technology to advance cancer research.

Cell Surface Transporters and Novel Drug Developments.

Despite the numerous scientific and technological advances made within the last decade the attrition rates for new drug discovery remain as high as 95% for anticancer drugs. Recent drug development has been in part guided by Lipinski’s Rule of 5 (Ro5) even though many approved drugs do not comply to these rules. With Covid-19 vaccine development strategy dramatically accelerating drug development perhaps it is timely to question the generic drug development process itself to find a more efficient, cost effective, and successful approach. It is widely believed that drugs permeate cells via two methods: phospholipid bilayer diffusion and carrier mediated transporters. However, emerging evidence suggests that carrier mediated transport may be the primary mechanism of drug uptake and not diffusion as long believed. Computational biology increasingly assists drug design to achieve desirable absorption, distribution, metabolism, elimination and toxicity (ADMET) properties. Perfecting drug entry into target cells as a prerequisite to intracellular drug action is a logical and compelling route and is expected to reduce drug attrition rates, particularly gaining favour amongst chronic lifelong therapeutics. Novel drug development is rapidly expanding from the utilisation of beyond the rule of five (bRo5) to pulsatile drug delivery systems and fragment based drug design. Utilising transporters as drug targets and advocating bRo5 molecules may be the solution to increasing drug specificity, reducing dosage and toxicity and thus revolutionising drug development. This review explores the development of cell surface transporter exploitation in drug development and the relationship with improved therapeutic index.

The Dawn of a New Era in Drug Discovery? Drug Screening and the Increasing Biological Complexity of Testing Models.

Whole-organism phenotype screening and complex in vitro model technology increase the likelihood in identifying successful lead compounds and lower drug attrition rates at later and more expensive stages of the drug discovery process.

Carboplatin response in preclinical models for ovarian cancer: comparison of 2D monolayers, spheroids, ex vivo tumors and in vivo models

Epithelial ovarian cancer (EOC) is the most lethal gynecological cancer. Among the key challenges in developing effective therapeutics is the poor translation of preclinical models used in the drug discovery pipeline. This leaves drug attrition rates and costs at an unacceptably high level. Previous work has highlighted the discrepancies in therapeutic response between current in vitro and in vivo models. To address this, we conducted a comparison study to differentiate the carboplatin chemotherapy response across four different model systems including 2D monolayers, 3D spheroids, 3D ex vivo tumors and mouse xenograft models. We used six previously characterized EOC cell lines of varying chemosensitivity and performed viability assays for each model. In vivo results from the mouse model correlated with 2D response in 3/6 cell lines while they correlated with 3D spheroids and the ex vivo model in 4/6 and 5/5 cell lines, respectively. Our results emphasize the variability in therapeutic response across models and demonstrate that the carboplatin response in EOC cell lines cultured in a 3D ex vivo model correlates best with the in vivo response. These results highlight a more feasible, reliable, and cost-effective preclinical model with the highest translational potential for drug screening and prediction studies in EOC.

An Overview of Advanced In Vitro Corneal Models: Implications for Pharmacological Testing.

The cornea is an important barrier to consider when developing ophthalmic formulations, but proper modeling of this multilayered tissue remains a challenge. This is due to the varying properties associated with each layer in addition to the dynamics of the tear film. Hence, the most representative models to date rely on animals. Animal models, however, differ from humans in several aspects and are subject to ethical limitations. Consequently, in vitro approaches are being developed to address these issues. This review focuses on the barrier properties of the cornea and evaluates the most advanced three-dimensional cultures of human corneal equivalents in literature. Their application potential is subsequently assessed and discussed in the context of preclinical testing along with our perspective toward the future. Impact statement Most ocular drugs are applied topically, with the transcorneal pathway as the main administration route. Animal corneas are currently the only advanced models available, contributing to the drug attrition rate. Anatomical and physiological interspecies differences might account for a poor translatability of preclinical results to clinical trials, urging researchers to devise better corneal equivalents. This review elaborates on the emerging generation of three-dimensional in vitro models, which comprises spheroids, organoids, and organs-on-chips, which can serve as a stepping stone for advancements in this field.

Coherent Raman Scattering Microscopy in Oncology Pharmacokinetic Research.

The high attrition rates of anti-cancer drugs during clinical development remains a bottleneck problem in pharmaceutical industry. This is partially due to the lack of quantitative, selective, and rapid readouts of anti-cancer drug activity in situ with high resolution. Although fluorescence microscopy has been commonly used in oncology pharmacological research, fluorescent labels are often too large in size for small drug molecules, and thus may disturb the function or metabolism of these molecules. Such challenge can be overcome by coherent Raman scattering microscopy, which is capable of chemically selective, highly sensitive, high spatial resolution, and high-speed imaging, without the need of any labeling. Coherent Raman scattering microscopy has tremendously improved the understanding of pharmaceutical materials in the solid state, pharmacokinetics of anti-cancer drugs and nanocarriers in vitro and in vivo. This review focuses on the latest applications of coherent Raman scattering microscopy as a new emerging platform to facilitate oncology pharmacokinetic research.

Batch Effect Adjustment to Lower the Drug Attrition Rate of MCF-7 Breast Cancer Cells Exposed to Silica Nanomaterial-Derived Scaffolds.

Drug attrition rate is the calculation or measure of the clinical efficacy of a candidate drug on a screen platform for a specific period. Determining the attrition rate of a prospective cancer drug is a reliable way of testing the clinical efficacy. A low attrition rate in the last phase of a preclinical trial increases the success of a drug discovery process. It has been reported that the attrition rates of antineoplastic drugs are much higher than for other therapeutic drugs. Among the factors identified for the high attrition rates in antineoplastic drugs are the nature of the screen-based platforms involving human-derived xenografts, extracellular matrix-derived scaffold systems, and the synthetic scaffolds, which all have propensity to proliferate tumor cells at faster rates than in vivo primary tumors. Other factors that affect the high attrition rates are induced scaffold toxicity and the use of assays that are irrelevant, yet affect data processing. These factors contribute to the wide variation in data and systematic errors. As a result, it becomes imperative to filter batch variations and to standardize the data. Importantly, understanding the interplay between the biological milieu and scaffold connections is also crucial. Here the cell viability of MCF-7 (breast cancer cell line) cells exposed to different scaffolds were screened before cisplatin dosing using the calculated p-values. The statistical significance (p-value) of data was calculated using the one-way analysis of variance, with the p-value set as: 0 < p < 0.06. In addition, the half-maximal inhibitory concentration (IC50) of the different scaffolds exposed to MCF-7 cells were calculated with the probit extension model and cumulative distribution (%) of the extension data. The chemotherapeutic dose (cisplatin, 56 mg/m2) reduced the cell viability of MCF-7 cells to 5% within 24 h on the scaffold developed from silica nanoparticles (SNPs) and polyethylene glycol (PEG) formulation (SNP:PEG) mixtures with a ratio of 1:10, respectively.

Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) is one of the most cited reasons for the high drug attrition rate and drug withdrawal from the market. The accumulated large amount of high throughput transcriptomic profiles and advances in deep learning provide an unprecedented opportunity to improve the suboptimal performance of DILI prediction. In this study, we developed an eight-layer Deep Neural Network (DNN) model for DILI prediction using transcriptomic profiles of human cell lines (LINCS L1000 dataset) with the current largest binary DILI annotation data [i.e., DILI severity and toxicity (DILIst)]. The developed models were evaluated by Monte Carlo cross-validation (MCCV), permutation test, and an independent validation (IV) set. The developed DNN model achieved the area under the receiver operating characteristic curve (AUC) of 0.802 and 0.798, and balanced accuracy of 0.741 and 0.721 for training and an IV set, respectively, outperforming the conventional machine learning algorithms, including K-nearest neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF). Moreover, the developed DNN model provided a more balanced sensitivity of 0.839 and specificity of 0.603. Besides, we found the developed DNN model had a superior predictive performance for oncology drugs. Also, the functional and network analysis of genes driving the predictions revealed their relevance to the underlying mechanisms of DILI. The proposed DNN model could be a promising tool for early detection of DILI potential in the pre-clinical setting.

Translational approach from preclinical to clinical: comparison of dose finding methods of a new Bcl2 inhibitor using PK-PD modeling and interspecies extrapolation.

The attrition rate of anticancer drugs during the clinical development remains very high. Interspecies extrapolation of anticancer drug pharmacodynamics (PD) could help to bridge the gap between preclinical and clinical settings and to improve drug development. Indeed, when combined with a physiologically-based-pharmacokinetics (PBPK) approach, PD interspecies extrapolation could be a powerful tool for predicting drug behavior in clinical trials. The present study aimed to explore this field for anticipating the clinical efficacy of a new Bcl-2 inhibitor, S 55746, for which dose ranging studies in xenografted mice and clinical data from a phase 1 trial involving cancer patients were available. Different strategies based on empirical or more mechanistic assumptions (based on PBPK-PD modelling) were developped and compared: the Rocchetti approach (ROC); the Orthogonal Rocchetti approach (oROC), a variant of ROC based on an orthogonal regression; the Consistent across species approach, bringing out an efficacy parameter assumed to be consistent across species; and the Scaling species-specific parameters approach, assuming the concentration-efficacy link is the same in mice as in humans, after allometric scaling. Empirical approaches (ROC and oROC) gave similar predictive performances and seemed to overestimate the active S 55746 dose compared to mechanistic approaches, while strategies elaborated from semi-mechanistic concepts and PBPK-PD modelling did not seem to be invalidated by clinical efficacy data. Also, empirical methods only predict a single dose level for the subsequent clinical studies, whereas mechanism-based strategies are more informative about the dose response relationship, highlighting the potential interest of such approaches in drug development.

Effects of 5-HT2C receptor modulation and the NA reuptake inhibitor atomoxetine in tests of compulsive and impulsive behaviour

Drug repositioning has gained strategic value as a reaction to high attrition rates of new drugs as they pass through the clinical development process. The 5-HT2C receptor agonist lorcaserin (Belviq®), and the selective NA reuptake inhibitor atomoxetine (Strattera®) represent two drugs FDA approved for obesity and ADHD respectively. Although both drugs are of differing pharmacological class, each share a property of regulating impulsive behaviours in preclinical studies, and thus represent candidates for consideration in clinical conditions labelled as ‘impulsive-compulsive disorders’. The present studies investigated both drugs, as well as the highly selective 5-HT2C agonist CP-809101 in two tests of compulsive action: schedule-induced polydipsia (SIP) and increased perseverative [PSV] (and premature [PREM]) responses emitted during an extended ITI 5-choice task. While lorcaserin (0.06–0.6 mg/kg), CP-809101 (0.1–1 mg/kg) and atomoxetine (0.1–1 mg/kg) each reduced both PREM and PSV measures in the 5-choice task, at equivalent doses only lorcaserin and CP-809101 affected excessive water intake in the SIP task, atomoxetine (0.1–2 mg/kg) was essentially ineffective. Further evidence supporting a role of the 5-HT2C receptor as an important regulator of impulsive-compulsive behaviours, the selective antagonist SB-242084 produced the opposing effects to lorcaserin, i.e promoting both impulsive and compulsive behaviours. The profile of atomoxetine may suggest differences in the nature of compulsive action measured either as non-regulatory drinking in the SIP task, and PSV responses made in a 5-choice task. These studies support the consideration of 5-HT2C receptor agonists, typified by lorcaserin, and atomoxetine as potential treatments for clinical conditions categorised as ‘impulsive-compulsive disorders’.This article is part of the special issue entitled ‘Serotonin Research: Crossing Scales and Boundaries’.

On the Role of Artificial Intelligence in Genomics to Enhance Precision Medicine.

The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.