A Novel Automated Framework for QSAR Modeling of Highly Imbalanced Leishmania High-Throughput Screening Data.

In silico prediction of antileishmanial activity using quantitative structure-activity relationship (QSAR) models has been developed on limited and small datasets. Nowadays, the availability of large and diverse high-throughput screening data provides an opportunity to the scientific community to model this activity from the chemical structure. In this study, we present the first KNIME automated workflow to modeling a large, diverse, and highly imbalanced dataset of compounds with antileishmanial activity. Because the data is strongly biased toward inactive compounds, a novel strategy was implemented based on the selection of different balanced training sets and a further consensus model using single decision trees as the base model and three criteria for output combinations. The decision tree consensus was adopted after comparing its classification performance to consensuses built upon Gaussian-Naïve-Bayes, Support-Vector-Machine, Random-Forest, Gradient-Boost, and Multi-Layer-Perceptron base models. All these consensuses were rigorously validated using internal and external test validation sets and were compared against each other using Friedman and Bonferroni-Dunn statistics. For the retained decision tree-based consensus model, which covers 100% of the chemical space of the dataset and with the lowest consensus level, the overall accuracy statistics for test and external sets were between 71 and 74% and 71 and 76%, respectively, while for a reduced chemical space (21%) and with an incremental consensus level, the accuracy statistics were substantially improved with values for the test and external sets between 86 and 92% and 88 and 92%, respectively. These results highlight the relevance of the consensus model to prioritize a relatively small set of active compounds with high prediction sensitivity using the Incremental Consensus at high level values or to predict as many compounds as possible, lowering the level of Incremental Consensus. Finally, the workflow developed eliminates human bias, improves the procedure reproducibility, and allows other researchers to reproduce our design and use it in their own QSAR problems.

Could climate change benefit invasive snakes? Modelling the potential distribution of the California Kingsnake in the Canary Islands.

The interaction between climate change and biological invasions is a global conservation challenge with major consequences for invasive species management. However, our understanding of this interaction has substantial knowledge gaps; this is particularly relevant for invasive snakes on islands because they can be a serious threat to island ecosystems. Here we evaluated the potential influence of climate change on the distribution of invasive snakes on islands, using the invasion of the California kingsnake (Lampropeltis californiae) in Gran Canaria. We analysed the potential distribution of L. californiae under current and future climatic conditions in the Canary Islands, with the underlying hypothesis that the archipelago might be suitable for the species under these climate scenarios. Our results indicate that the Canary Islands are currently highly suitable for the invasive snake, with increased suitability under the climate change scenarios tested here. This study supports the idea that invasive reptiles represent a substantial threat to near-tropical regions, and builds on previous studies suggesting that the menace of invasive reptiles may persist or even be exacerbated by climate change. We suggest future research should continue to fill the knowledge gap regarding invasive reptiles, in particular snakes, to clarify their potential future impacts on global biodiversity.

ADME Prediction with KNIME: Development and Validation of a Publicly Available Workflow for the Prediction of Human Oral Bioavailability.

In silico prediction of human oral bioavailability is a relevant tool for the selection of potential drug candidates and for the rejection of those molecules with less probability of success during the early stages of drug discovery and development. However, the high variability and complexity of oral bioavailability and the limited experimental data in the public domain have mainly restricted the development of reliable in silico models to predict this property from the chemical structure. In this study we present a KNIME automated workflow to predict human oral bioavailability of new drug and drug-like molecules based on five machine learning approaches combined into an ensemble model. The workflow is freely accessible and allows the quick and easy prediction of oral bioavailability for new molecules. Users do not require any knowledge or advanced experience in machine learning or statistical modeling to automatically obtain their predictions, increasing the potential use of the present proposal.

Integrating theoretical and experimental permeability estimations for provisional biopharmaceutical classification: Application to the WHO essential medicines.

The accuracy of the provisional estimation of the Biopharmaceutics Classification System (BCS) is heavily influenced by the permeability measurement. In this study, several theoretical and experimental models currently employed for BCS permeability classification have been analysed. The experimental models included the in situ rat intestinal perfusion, the ex vivo rat intestinal tissue in an Ussing chamber, the MDCK and Caco-2 cell monolayers, and the parallel artificial membrane (PAMPA). The theoretical models included the octanol-water partition coefficient and the QSPeR (Quantitative Structure-Permeability Relationship) model recently developed. For model validation, a dataset of 43 compounds has been recompiled and analysed for the suitability for BCS permeability classification in comparison with the use of human intestinal absorption and oral bioavailability values. The application of the final model, based on a majority voting system showed a 95.3% accuracy for predicting human permeability. Finally, the present approach was applied to the 186 orally administered drugs in immediate-release dosage forms of the WHO Model List of Essential Medicines. The percentages of the drugs that were provisionally classified as BCS Class I and Class III was 62.4%, suggesting that in vivo bioequivalence (BE) may potentially be assured with a less expensive and more easily implemented in vitro dissolution test, ensuring the efficiency and quality of pharmaceutical products. The results of the current study improve the accuracy of provisional BCS classification by combining different permeability models.

Biowaiver or Bioequivalence: Ambiguity in Sildenafil Citrate BCS Classification.

The aim of the present study is to contribute to the scientific characterization of sildenafil citrate according to the Biopharmaceutics Classification System, following the World Health Organization (WHO) guidelines for biowaivers. The solubility and intestinal permeability data of sildenafil citrate were collected from literature; however, the experimental solubility studies are inconclusive and its "high permeability" suggests an API in the borderline of BCS Class I and Class II. The pH-solubility profile was determined using the saturation shake-flask method over the pH range of 1.2-6.8 at a temperature of 37 °C in aqueous media. The intestinal permeability was determined in rat by a closed-loop in situ perfusion method (the Doluisio technique). The solubility of sildenafil citrate is pH-dependent and at pH 6.8 the dose/solubility ratio obtained does not meet the WHO criteria for "high solubility." The high permeability values obtained by in situ intestinal perfusion in rat reinforce the published permeability data for sildenafil citrate. The experimental results obtained and the data available in the literature suggest that sildenafil citrate is clearly a Class II of BCS, according to the current biopharmaceutics classification system and WHO guidance.

Exploring different strategies for imbalanced ADME data problem: case study on Caco-2 permeability modeling.

In many absorption, distribution, metabolism, and excretion (ADME) modeling problems, imbalanced data could negatively affect classification performance of machine learning algorithms. Solutions for handling imbalanced dataset have been proposed, but their application for ADME modeling tasks is underexplored. In this paper, various strategies including cost-sensitive learning and resampling methods were studied to tackle the moderate imbalance problem of a large Caco-2 cell permeability database. Simple physicochemical molecular descriptors were utilized for data modeling. Support vector machine classifiers were constructed and compared using multiple comparison tests. Results showed that the models developed on the basis of resampling strategies displayed better performance than the cost-sensitive classification models, especially in the case of oversampling data where misclassification rates for minority class have values of 0.11 and 0.14 for training and test set, respectively. A consensus model with enhanced applicability domain was subsequently constructed and showed improved performance. This model was used to predict a set of randomly selected high-permeability reference drugs according to the biopharmaceutics classification system. Overall, this study provides a comparison of numerous rebalancing strategies and displays the effectiveness of oversampling methods to deal with imbalanced permeability data problems.

Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands.

Telomeres and telomerase are key players in tumorogenesis. Among the various strategies proposed for telomerase inhibition or telomere uncapping, the stabilization of telomeric G-quadruplex (G4) structures is a very promising one. Additionally, G4 stabilizing ligands also act over tumors mediated by the alternative elongation of telomeres. Accordingly, the discovery of novel compounds able to act on telomeres and/or inhibit the telomerase enzyme by stabilizing DNA telomeric G4 structures as well as the development of approaches efficiently prioritizing such compounds constitute active areas of research in computational medicinal chemistry and anticancer drug discovery. In this direction, we applied a virtual screening strategy based on the rigorous application of QSAR best practices and its harmonized integration with structure-based methods. More than 600,000 compounds from commercial databases were screened, the first 99 compounds were prioritized, and 21 commercially available and structurally diverse candidates were purchased and submitted to experimental assays. Such strategy proved to be highly efficient in the prioritization of G4 stabilizer hits, with a hit rate of 23.5%. The best G4 stabilizer hit found exhibited a shift in melting temperature from FRET assay of +7.3 °C at 5 µM, while three other candidates also exhibited a promising stabilizing profile. The two most promising candidates also exhibited a good telomerase inhibitory ability and a mild inhibition of HeLa cells growth. None of these candidates showed antiproliferative effects in normal fibroblasts. Finally, the proposed virtual screening strategy proved to be a practical and reliable tool for the discovery of novel G4 ligands which can be used as starting points of further optimization campaigns.

The efficacy of 2-nitrovinylfuran derivatives against Leishmania in vitro and in vivo.

Despite recent advances in the treatment of some forms of leishmaniasis, the available drugs are still far from ideal due to inefficacy, parasite resistance, toxicity and cost. The wide-spectrum antimicrobial activity of 2-nitrovinylfuran compounds has been described, as has their activity against Trichomonas vaginalis and other protozoa. Thus, the aim of this study was to test the antileishmanial activities of six 2-nitrovinylfurans in vitro and in a murine model of leishmaniasis. Minimum parasiticide concentration (MPC) and 50% inhibitory concentration (IC50) values for these compounds against the promastigotes of Leishmania amazonensis, Leishmania infantum and Leishmania braziliensis were determined, as were the efficacies of two selected compounds in an experimental model of cutaneous leishmaniasis (CL) caused by L. amazonensis in BALB/c mice. All of the compounds were active against the promastigotes of the three Leishmania species tested. IC50 and MPC values were in the ranges of 0.8-4.7 µM and 1.7-32 µM, respectively. The compounds 2-bromo-5-(2-bromo-2-nitrovinyl)-furan (furvina) and 2-bromo-5-(2-methyl-2-nitrovinyl)-furan (UC245) also reduced lesion growth in vivo at a magnitude comparable to or higher than that achieved by amphotericin B treatment. The results demonstrate the potential of this class of compounds as antileishmanial agents and support the clinical testing of Dermofural(r) (a furvina-containing antifungal ointment) for the treatment of CL.

Toward the computer-aided discovery of FabH inhibitors. Do predictive QSAR models ensure high quality virtual screening performance?

Antibiotic resistance has increased over the past two decades. New approaches for the discovery of novel antibacterials are required and innovative strategies will be necessary to identify novel and effective candidates. Related to this problem, the exploration of bacterial targets that remain unexploited by the current antibiotics in clinical use is required. One of such targets is the ß-ketoacyl-acyl carrier protein synthase III (FabH). Here, we report a ligand-based modeling methodology for the virtual-screening of large collections of chemical compounds in the search of potential FabH inhibitors. QSAR models are developed for a diverse dataset of 296 FabH inhibitors using an in-house modeling framework. All models showed high fitting, robustness, and generalization capabilities. We further investigated the performance of the developed models in a virtual screening scenario. To carry out this investigation, we implemented a desirability-based algorithm for decoys selection that was shown effective in the selection of high quality decoys sets. Once the QSAR models were validated in the context of a virtual screening experiment their limitations arise. For this reason, we explored the potential of ensemble modeling to overcome the limitations associated to the use of single classifiers. Through a detailed evaluation of the virtual screening performance of ensemble models it was evidenced, for the first time to our knowledge, the benefits of this approach in a virtual screening scenario. From all the obtained results, we could arrive to a significant main conclusion: at least for FabH inhibitors, virtual screening performance is not guaranteed by predictive QSAR models.

An open dataset of anticoagulant rodenticides in liver samples from California kingsnakes and raptors in Gran Canaria (Canary Islands, Spain).

It is well known that rodenticides are widely used, and there are multiple routes by which they can reach non-target wildlife species. Specifically, in the Canary Islands, a high and concerning incidence of these compounds has been reported. However, in this scenario, reptiles remain one of the least studied taxa, despite their potential suitability as indicators of the food chain and environmental pollution has been noted on several occasions. In this context, the California Kingsnake (Lampropeltis Californiae), widely distributed on the island of Gran Canaria, occupies a medium trophic level and exhibits feeding habits that expose it to these pollutants, could be studied as a potential sentinel of exposure to these compounds. For this reason, 360 snake livers were analyzed by LC-MS/MS. Similarly, 110 livers of birds of prey were sampled. Thus, we present the analysis of 10 anticoagulant rodenticides (warfarin, diphacinone, chlorophacinone, coumachlor, coumatetralyl, brodifacoum, bromadiolone, difethialone, difenacoum and flocoumafen) in both data series; snakes, and raptors. Furthermore, this dataset includes biological data (weight, length, sex, colour, and design pattern), geographic data (distribution area and municipalities) and necropsy findings that could be of interest for a better understanding of this snake species and for future studies.

Provisional classification and in silico study of biopharmaceutical system based on caco-2 cell permeability and dose number.

Today, early characterization of drug properties by the Biopharmaceutics Classification System (BCS) has attracted significant attention in pharmaceutical discovery and development. In this direction, the present report provides a systematic study of the development of a BCS-based provisional classification (PBC) for a set of 322 oral drugs. This classification, based on the revised aqueous solubility and the apparent permeability across Caco-2 cell monolayers, displays a high correlation (overall 76%) with the provisional BCS classification published by World Health Organization (WHO). Current database contains 91 (28.3%) PBC class I drugs, 76 (23.6%) class II drugs, 97 (31.1%) class III drugs, and 58 (18.0%) class IV drugs. Other approaches for provisional classification of drugs have been surveyed. The use of a calculated polar surface area with a labetalol value as a high permeable cutoff limit and aqueous solubility higher than 0.1 mg/mL could be used as alternative criteria for provisionally classifying BCS permeability and solubility in early drug discovery. To develop QSPR models that allow screening PBC and BCS classes of new molecular entities (NMEs), 18 statistical linear and nonlinear models have been constructed based on 803 0-2D Dragon and 126 Volsurf+ molecular descriptors to classify the PBC solubility and permeability. The voting consensus model of solubility (VoteS) showed a high accuracy of 88.7% in training and 92.3% in the test set. Likewise, for the permeability model (VoteP), accuracy was 85.3% in training and 96.9% in the test set. A combination of VoteS and VoteP appropriately predicts the PBC class of drugs (overall 73% with class I precision of 77.2%). This consensus system predicts an external set of 57 WHO BCS classified drugs with 87.5% of accuracy. Interestingly, computational assignments of the PBC class reasonably correspond to the Biopharmaceutics Drug Disposition Classification System (BDDCS) allocations of drugs (accuracy of 63.3-69.8%). A screening assay has been simulated using a large data set of compounds in different drug development phases (1, 2, 3, and launched) and NMEs. Distributions of PBC forecasts illustrate the current status in drug discovery and development. It is anticipated that a combination of the QSPR approach and well-validated in vitro experimentations could offer the best estimation of BCS for NMEs in the early stages of drug discovery.

GA(M)E-QSAR: a novel, fully automatic genetic-algorithm-(meta)-ensembles approach for binary classification in ligand-based drug design.

Computer-aided drug design has become an important component of the drug discovery process. Despite the advances in this field, there is not a unique modeling approach that can be successfully applied to solve the whole range of problems faced during QSAR modeling. Feature selection and ensemble modeling are active areas of research in ligand-based drug design. Here we introduce the GA(M)E-QSAR algorithm that combines the search and optimization capabilities of Genetic Algorithms with the simplicity of the Adaboost ensemble-based classification algorithm to solve binary classification problems. We also explore the usefulness of Meta-Ensembles trained with Adaboost and Voting schemes to further improve the accuracy, generalization, and robustness of the optimal Adaboost Single Ensemble derived from the Genetic Algorithm optimization. We evaluated the performance of our algorithm using five data sets from the literature and found that it is capable of yielding similar or better classification results to what has been reported for these data sets with a higher enrichment of active compounds relative to the whole actives subset when only the most active chemicals are considered. More important, we compared our methodology with state of the art feature selection and classification approaches and found that it can provide highly accurate, robust, and generalizable models. In the case of the Adaboost Ensembles derived from the Genetic Algorithm search, the final models are quite simple since they consist of a weighted sum of the output of single feature classifiers. Furthermore, the Adaboost scores can be used as ranking criterion to prioritize chemicals for synthesis and biological evaluation after virtual screening experiments.

Reliable Prediction of Caco-2 Permeability by Supervised Recursive Machine Learning Approaches.

The heterogeneity of the Caco-2 cell line and differences in experimental protocols for permeability assessment using this cell-based method have resulted in the high variability of Caco-2 permeability measurements. These problems have limited the generation of large datasets to develop accurate and applicable regression models. This study presents a QSPR approach developed on the KNIME analytical platform and based on a structurally diverse dataset of over 4900 molecules. Interpretable models were obtained using random forest supervised recursive algorithms for data cleaning and feature selection. The development of a conditional consensus model based on regional and global regression random forest produced models with RMSE values between 0.43-0.51 for all validation sets. The potential applicability of the model as a surrogate for the in vitro Caco-2 assay was demonstrated through blind prediction of 32 drugs recommended by the International Council for the Harmonization of Technical Requirements for Pharmaceuticals (ICH) for validation of in vitro permeability methods. The model was validated for the preliminary estimation of the BCS/BDDCS class. The KNIME workflow developed to automate new drug prediction is freely available. The results suggest that this automated prediction platform is a reliable tool for identifying the most promising compounds with high intestinal permeability during the early stages of drug discovery.

Integration of In Silico, In Vitro and In Situ Tools for the Preformulation and Characterization of a Novel Cardio-Neuroprotective Compound during the Early Stages of Drug Development.

The main aim of this work is the biopharmaceutical characterization of a new hybrid benzodiazepine-dihydropyridine derivative, JM-20, derived with potent anti-ischemic and neuroprotective effects. In this study, the pKa and the pH-solubility profile were experimentally determined. Additionally, effective intestinal permeability was measured using three in vitro epithelial cell lines (MDCK, MDCK-MDR1 and Caco-2) and an in situ closed-loop intestinal perfusion technique. The results indicate that JM-20 is more soluble at acidic pH (9.18 ± 0.16); however, the Dose number (Do) was greater than 1, suggesting that it is a low-solubility compound. The permeability values obtained with in vitro cell lines as well as with the in situ perfusion method show that JM-20 is a highly permeable compound (Caco-2 value 3.8 × 10-5). The presence of an absorption carrier-mediated transport mechanism was also demonstrated, as well as the efflux effect of P-glycoprotein on the permeability values. Finally, JM-20 was provisionally classified as class 2 according to the biopharmaceutical classification system (BCS) due to its high intestinal permeability and low solubility. The potential good oral absorption of this compound could be limited by its solubility.

Molecular dynamics and docking simulations as a proof of high flexibility in E. coli FabH and its relevance for accurate inhibitor modeling.

Bacterial ß-ketoacyl-acyl carrier protein synthase III (FabH) has become an attractive target for the development of new antibacterial agents which can overcome the increased resistance of these pathogens to antibiotics in clinical use. Despite several efforts have been dedicated to find inhibitors for this enzyme, it is not a straightforward task, mainly due its high flexibility which makes difficult the structure-based design of FabH inhibitors. Here, we present for the first time a Molecular Dynamics (MD) study of the E. colil FabH enzyme to explore its conformational space. We compare the flexibility of this enzyme for the unliganded protein and an enzyme-inhibitor complex and find a correspondence between our modeling results and the experimental evidence previously reported for this enzyme. Furthermore, through a 100 ns MD simulation of the unliganded enzyme we extract useful information related to the concerted motions that take place along the principal components of displacement. We also establish a relation between the presence of water molecules in the oxyanion hole with the conformational stability of structural important loops. Representative conformations of the binding pocket along the whole trajectory of the unliganded protein are selected through cluster analysis and we find that they contain a conformational diversity which is not provided by the X-ray structures of ecFabH. As a proof of this last hypothesis, we use a set of 10 FabH inhibitors and show that they cannot be correctly modeled in any available X-ray structure, while by using our set of conformations extracted from the MD simulations, this task can be accomplish. Finally, we show the ability of short MD simulations for the refinement of the docking binding poses and for MM-PBSA calculations to predict stable protein-inhibitor complexes in this enzyme.

ADME prediction with KNIME: A retrospective contribution to the second "Solubility Challenge".

Computational models for predicting aqueous solubility from the molecular structure represent a promising strategy from the perspective of drug design and discovery. Since the first "Solubility Challenge", these initiatives have marked the state-of-art of the modelling algorithms used to predict drug solubility. In this regard, the quality of the input experimental data and its influence on model performance has been frequently discussed. In our previous study, we developed a computational model for aqueous solubility based on recursive random forest approaches. The aim of the current commentary is to analyse the performance of this already trained predictive model on the molecules of the second "Solubility Challenge". Even when our training set has inconsistencies related to the pH, solid form and temperature conditions of the solubility measurements, the model was able to predict the two sets from the second "Solubility Challenge" with statistics comparable to those of the top ranked models. Finally, we provided a KNIME automated workflow to predict aqueous solubility of new drug candidates, during the early stages of drug discovery and development, for ensuring the applicability and reproducibility of our model.

ICH Guideline for Biopharmaceutics Classification System-Based Biowaiver (M9): Toward Harmonization in Latin American Countries.

The biopharmaceutical classification system (BCS) is a very important tool to replace the traditional in vivo bioequivalence studies with in vitro dissolution assays during multisource product development. This paper compares the most recent harmonized guideline for biowaivers based on the biopharmaceutics classification system and the BCS regulatory guidelines in Latin America and analyzes the current BCS regulatory requirements and the perspective of the harmonization in the region to develop safe and effective multisource products. Differences and similarities between the official and publicly available BCS guidelines of several Latin American regulatory authorities and the new ICH harmonization guideline were identified and compared. Only Chile, Brazil, Colombia, and Argentina have a more comprehensive BCS guideline, which includes solubility, permeability, and dissolution requirements. Although their regulatory documents have many similarities with the ICH guidelines, there are still major differences in their interpretation and application. This situation is an obstacle to the successful development of safe and effective multisource products in the Latin American region, not only to improve their access to patients at a reasonable cost, but also to develop BCS biowaiver studies that fulfill the quality standards of regulators in developed and emerging markets.

Policy of Multisource Drug Products in Latin America: Opportunities and Challenges on the Application of Bioequivalence In Vitro Assays.

BACKGROUND: The replacement of traditional in vivo bioequivalence studies by in vitro dissolution assays, based on the biopharmaceutical classification system (BCS), has emerged as an important tool for demonstrating the interchangeability of multisource products. This paper summarizes the current implementation of the BCS-based biowaiver for the development of multisource products in Latin America, and identifies several challenges and opportunities for greater convergence and application of BCS regulatory requirements. METHODS: Differences and similarities between the current BCS-based biowaivers' guidelines proposed by two relevant regulatory agencies for the Latin American region (FDA and WHO) and the new ICH harmonization guideline were identified and compared. An update of the BCS-based biowaiver guideline for Latin American countries was also considered, based on the respective regulatory information on bioequivalence studies, which is publicly available. RESULTS: About 50% of the Latin American countries analyzed have no information on the implementation of any bioequivalence standards, while in the countries where bioequivalence studies are considered, the acceptance and application of BCS-based biowaiver requirements is quite heterogeneous. This situation contrasts with the international trend of global harmonization for BCS-based biowaiver guidance, suggesting the need in Latin America to identify opportunities and overcome challenges to improve the development of BCS-based biowaivers to avoid costly and time-consuming in vivo bioequivalence studies. CONCLUSIONS: The study shows that the region is in a position to improve access to safe and effective medicines at a reasonable cost by applying BCS-based biowaiver guidance.

Epidemiology of Animal Poisonings in the Canary Islands (Spain) during the Period 2014-2021.

Animal poisoning is one of the greatest conservation threats facing wildlife. In a preliminary study in the oceanic archipelago of the Canary Islands, we showed that the degree of threat from this circumstance was very high-even higher than that reported in other regions of continental Europe. Consequently, a legal framework for the effective prosecution of the crime of wildlife poisoning came into force in 2014 in this region. We present the results of the investigation of 961 animals and 84 baits sent to our laboratory for the diagnosis of animal poisonings during the period 2014-2021. We were able to identify poison as the cause of death in 251 animals and 61 baits. Carbofuran stands out as the main agent used in this archipelago. We have also detected an increasing tendency to use mixtures of several pesticides in the preparation of baits. The entry into operation of two canine patrols has led to the detection of more dead animals in the wild and a greater number of poisoned animals. The percentage of poison positives is significantly higher in areas with lower population density, corresponding to rural environments, as well as in areas with greater agricultural and livestock activity.

Dataset on the concentrations of anticoagulant rodenticides in raptors from the Canary Islands with geographic information.

The dataset presented in this article supports "Intensive livestock farming as a major determinant of the exposure to anticoagulant rodenticides in raptors of the Canary Islands (Spain)" (Rial-Berriel et al., 2020). A Geographic Information System (GIS) analysis on the influence of the influence of livestock activity on exposure to anticoagulant rodenticides in raptors in the Canary Islands was performed. This dataset provides geographic information on the localization of each raptor (either positive or negative for anticoagulant rodenticides, n = 308), as well as the concentrations of each compound found in their livers. In addition, we present complementary analyses to those included in the main article, such as the detailed analysis of the farming activity influence on anticoagulant rodenticide exposure of raptors, by island and by raptor species.