Ernst Ahlberg

The EU-ADR Web Platform: delivering advanced pharmacovigilance tools

Pharmacovigilance methods have advanced greatly during the last decades, making post‐market drug assessment an essential drug evaluation component. These methods mainly rely on the use of spontaneous reporting systems and health information databases to collect expertise from huge amounts of real‐world reports. The EU‐ADR Web Platform was built to further facilitate accessing, monitoring and exploring these data, enabling an in‐depth analysis of adverse drug reactions risks.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscopes expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated.

Computational derivation of structural alerts from large toxicology data sets.

Structural alerts have been one of the backbones of computational toxicology and have applications in many areas including cosmetic, environmental, and pharmaceutical toxicology. The development of structural alerts has always involved a manual analysis of existing data related to a relevant end point followed by the determination of substructures that appear to be related to a specific outcome. The substructures are then analyzed for their utility in posterior validation studies, which at times have stretched over years or even decades. With higher throughput methods now being employed in many areas of toxicology, data sets are growing at an unprecedented rate. This growth has made manual analysis of data sets impractical in many cases. This report outlines a fully automatic method that highlights significant substructures for toxicologically important data sets. The method identifies important substructures by computationally breaking chemical structures into fragments and analyzing those fragments for their contribution to the given activity by the calculation of a p-value and a substructure accuracy. The method is intended to aid the expert in locating and analyzing alerts by automatic retrieval of alerts or by enhancing existing alerts. The method has been applied to a data set of AMES mutagenicity results and compared to the substructures generated by manual curation of this same data set as well as another computationally based substructure identification method. The results show that this method can retrieve significant substructures quickly, that the substructures are comparable and in some cases superior to those derived from manual curation, that the substructures found covers all previously known substructures, and that they can be used to make reasonably accurate predictions of AMES activity.

Gathering and exploring scientific knowledge in pharmacovigilance.

Pharmacovigilance plays a key role in the healthcare domain through the assessment, monitoring and discovery of interactions amongst drugs and their effects in the human organism. However, technological advances in this field have been slowing down over the last decade due to miscellaneous legal, ethical and methodological constraints. Pharmaceutical companies started to realize that collaborative and integrative approaches boost current drug research and development processes. Hence, new strategies are required to connect researchers, datasets, biomedical knowledge and analysis algorithms, allowing them to fully exploit the true value behind state-of-the-art pharmacovigilance efforts. This manuscript introduces a new platform directed towards pharmacovigilance knowledge providers. This system, based on a service-oriented architecture, adopts a plugin-based approach to solve fundamental pharmacovigilance software challenges. With the wealth of collected clinical and pharmaceutical data, it is now possible to connect knowledge providers’ analysis and exploration algorithms with real data. As a result, new strategies allow a faster identification of high-risk interactions between marketed drugs and adverse events, and enable the automated uncovering of scientific evidence behind them. With this architecture, the pharmacovigilance field has a new platform to coordinate large-scale drug evaluation efforts in a unique ecosystem, publicly available at http://bioinformatics.ua.pt/euadr/.

Localized Heuristic Inverse Quantitative Structure Activity Relationship with Bulk Descriptors Using Numerical Gradients

State-of-the-art quantitative structure-activity relationship (QSAR) models are often based on nonlinear machine learning algorithms, which are difficult to interpret. From a pharmaceutical perspective, QSARs are used to enhance the chemical design process. Ultimately, they should not only provide a prediction but also contribute to a mechanistic understanding and guide modifications to the chemical structure, promoting compounds with desirable biological activity profiles. Global ranking of descriptor importance and inverse QSAR have been used for these purposes. This paper introduces localized heuristic inverse QSAR, which provides an assessment of the relative ability of the descriptors to influence the biological response in an area localized around the predicted compound. The method is based on numerical gradients with parameters optimized using data sets sampled from analytical functions. The heuristic character of the method reduces the computational requirements and makes it applicable not only to fragment based methods but also to QSARs based on bulk descriptors. The application of the method is illustrated on congeneric QSAR data sets, and it is shown that the predicted influential descriptors can be used to guide structural modifications that affect the biological response in the desired direction. The method is implemented into the AZOrange Open Source QSAR package. The current implementation of localized heuristic inverse QSAR is a step toward a generally applicable method for elucidating the structure activity relationship specifically for a congeneric region of chemical space when using QSARs based on bulk properties. Consequently, this method could contribute to accelerating the chemical design process in pharmaceutical projects, as well as provide information that could enhance the mechanistic understanding for individual scaffolds.

Deconvoluting Kinase Inhibitor Induced Cardiotoxicity

Abstract Many drugs designed to inhibit kinases have their clinical utility limited by cardiotoxicity-related label warnings or prescribing restrictions. While this liability is widely recognized, designing safer kinase inhibitors (KI) requires knowledge of the causative kinase(s). Efforts to unravel the kinases have encountered pharmacology with nearly prohibitive complexity. At therapeutically relevant concentrations, KIs show promiscuity distributed across the kinome. Here, to overcome this complexity, 65 KIs with known kinome-scale polypharmacology profiles were assessed for effects on cardiomyocyte (CM) beating. Changes in human iPSC-CM beat rate and amplitude were measured using label-free cellular impedance. Correlations between beat effects and kinase inhibition profiles were mined by computation analysis (Matthews Correlation Coefficient) to identify associated kinases. Thirty kinases met criteria of having (1) pharmacological inhibition correlated with CM beat changes, (2) expression in both human-induced pluripotent stem cell-derived cardiomyocytes and adult heart tissue, and (3) effects on CM beating following single gene knockdown. A subset of these 30 kinases were selected for mechanistic follow up. Examples of kinases regulating processes spanning the excitation–contraction cascade were identified, including calcium flux (RPS6KA3, IKBKE) and action potential duration (MAP4K2). Finally, a simple model was created to predict functional cardiotoxicity whereby inactivity at three sentinel kinases (RPS6KB1, FAK, STK35) showed exceptional accuracy in vitro and translated to clinical KI safety data. For drug discovery, identifying causative kinases and introducing a predictive model should transform the ability to design safer KI medicines. For cardiovascular biology, discovering kinases previously unrecognized as influencing cardiovascular biology should stimulate investigation of underappreciated signaling pathways.

Interpretation of Conformal Prediction Classification Models

We present a method for interpretation of conformal prediction models. The discrete gradient of the largest p-value is calculated with respect to object space. A criterion is applied to identify the most important component of the gradient and the corresponding part of the object is visualized.

In silico toxicology protocols

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets

Conformal prediction has been proposed as a more rigorous way to define prediction confidence compared to other application domain concepts that have earlier been used for QSAR modeling. One main advantage of such a method is that it provides a prediction region potentially with multiple predicted labels, which contrasts to the single valued (regression) or single label (classification) output predictions by standard QSAR modeling algorithms. Standard conformal prediction might not be suitable for imbalanced data sets. Therefore, Mondrian cross-conformal prediction (MCCP) which combines the Mondrian inductive conformal prediction with cross-fold calibration sets has been introduced. In this study, the MCCP method was applied to 18 publicly available data sets that have various imbalance levels varying from 1:10 to 1:1000 (ratio of active/inactive compounds). Our results show that MCCP in general performed well on bioactivity data sets with various imbalance levels. More importantly, the method not only provides confidence of prediction and prediction regions compared to standard machine learning methods but also produces valid predictions for the minority class. In addition, a compound similarity based nonconformity measure was investigated. Our results demonstrate that although it gives valid predictions, its efficiency is much worse than that of model dependent metrics.

A novel multi-parametric high content screening assay in ciPTEC-OAT1 to predict drug-induced nephrotoxicity during drug discovery

Drug-induced nephrotoxicity is a major concern in the clinic and hampers the use of available treatments as well as the development of innovative medicines. It is typically discovered late during drug development, which reflects a lack of in vitro nephrotoxicity assays available that can be employed readily in early drug discovery, to identify and hence steer away from the risk. Here, we report the development of a high content screening assay in ciPTEC-OAT1, a proximal tubular cell line that expresses several relevant renal transporters, using five fluorescent dyes to quantify cell health parameters. We used a validation set of 62 drugs, tested across a relevant concentration range compared to their exposure in humans, to develop a model that integrates multi-parametric data and drug exposure information, which identified most proximal tubular toxic drugs tested (sensitivity 75%) without any false positives (specificity 100%). Due to the relatively high throughput (straight-forward assay protocol, 96-well format, cost-effective) the assay is compatible with the needs in the early drug discovery setting to enable identification, quantification and subsequent mitigation of the risk for nephrotoxicity.