CPSign: conformal prediction for cheminformatics modeling.

Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign implements inductive and transductive conformal prediction for classification and regression, and probabilistic prediction with the Venn-ABERS methodology.

Principles and Procedures for Assessment of Acute Toxicity Incorporating Methods.

Acute models are being used to support an increasing number of application areas including (1) product research and development, (2) product approval and registration as well as (3) the transport, storage and handling of chemicals. The adoption of such models is being hindered, in part, because of a lack of guidance describing how to perform and document an analysis. To address this issue, a framework for an acute toxicity hazard assessment is proposed.

How to Predict the p of Any Compound in Any Solvent.

Acid-base properties of molecules in nonaqueous solvents are of critical importance for almost all areas of chemistry. Despite this very high relevance, our knowledge is still mostly limited to the p of rather few compounds in the most common solvents, and a simple yet truly general computational procedure to predict p 's of any compound in any solvent is still missing. In this contribution, we describe such a procedure.

Machine Learning Strategies When Transitioning between Biological Assays.

Machine learning is widely used in drug development to predict activity in biological assays based on chemical structure. However, the process of transitioning from one experimental setup to another for the same biological endpoint has not been extensively studied. In a retrospective study, we here explore different modeling strategies of how to combine data from the old and new assays when training conformal prediction models using data from hERG and Na assays.

Skin sensitization in silico protocol.

The assessment of skin sensitization has evolved over the past few years to include in vitro assessments of key events along the adverse outcome pathway and opportunistically capitalize on the strengths of in silico methods to support a weight of evidence assessment without conducting a test in animals. While in silico methods vary greatly in their purpose and format; there is a need to standardize the underlying principles on which such models are developed and to make transparent the implications for the uncertainty in the overall assessment. In this contribution, the relationship between skin sensitization relevant effects, mechanisms, and endpoints are built into a hazard assessment framework.

Genetic toxicology in silico protocol.

In silico toxicology (IST) approaches to rapidly assess chemical hazard, and usage of such methods is increasing in all applications but especially for regulatory submissions, such as for assessing chemicals under REACH as well as the ICH M7 guideline for drug impurities. There are a number of obstacles to performing an IST assessment, including uncertainty in how such an assessment and associated expert review should be performed or what is fit for purpose, as well as a lack of confidence that the results will be accepted by colleagues, collaborators and regulatory authorities. To address this, a project to develop a series of IST protocols for different hazard endpoints has been initiated and this paper describes the genetic toxicity in silico (GIST) protocol.

Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors.

Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform.

Predicting Ames Mutagenicity Using Conformal Prediction in the Ames/QSAR International Challenge Project.

Valid and predictive models for classifying Ames mutagenicity have been developed using conformal prediction. The models are Random Forest models using signature molecular descriptors. The investigation indicates, on excluding not-strongly mutagenic compounds (class B), that the validity for mutagenic compounds is increased for the predictions based on both public and the Division of Genetics and Mutagenesis, National Institute of Health Sciences of Japan (DGM/NIHS) data while less so when using only the latter data source.

Predicting Off-Target Binding Profiles With Confidence Using Conformal Prediction.

Ligand-based models can be used in drug discovery to obtain an early indication of potential off-target interactions that could be linked to adverse effects. Another application is to combine such models into a panel, allowing to compare and search for compounds with similar profiles. Most contemporary methods and implementations however lack valid measures of confidence in their predictions, and only provide point predictions.

Predicting Aromatic Amine Mutagenicity with Confidence: A Case Study Using Conformal Prediction.

The occurrence of mutagenicity in primary aromatic amines has been investigated using conformal prediction. The results of the investigation show that it is possible to develop mathematically proven models using conformal prediction and that the existence of uncertain classes of prediction, such as (both classes assigned to a compound) and (no class assigned to a compound), provides the user with additional information on how to use, further develop, and possibly improve future models. The study also indicates that the use of different sets of fingerprints results in models, for which the ability to discriminate varies with respect to the set level of acceptable errors.

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.

In silico toxicology protocols.

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions.

Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen.

The drug-induced accumulation of phospholipids in lysosomes of various tissues is predominantly observed in regular repeat dose studies, often after prolonged exposure, and further investigated in mechanistic studies prior to candidate nomination. The finding can cause delays in the discovery process inflicting high costs to the affected projects. This article presents an in vitro imaging-based method for early detection of phospholipidosis liability and a hybrid approach for early detection and risk mitigation of phospolipidosis utilizing the in vitro readout with in silico model prediction.

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.

Deconvoluting Kinase Inhibitor Induced Cardiotoxicity.

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.

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.

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.

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.

The EU-ADR Web Platform: delivering advanced pharmacovigilance tools.

Purpose: 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.