The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints.

Matched molecular pair analysis (MMPA) has emerged as a powerful approach to mine and extract tacit knowledge from measured databases of small molecules. Extracted knowledge from past experimentation can assist future lead optimization as an idea generation tool and, hence, reduce the number of design-synthesis-test cycles. While attractive and intuitive, MMPA still presents several limitations. Analyses of internal absorption, distribution, metabolism, and excretion (ADME) databases of measured compounds show that chemical transformations with 10 pairs or more represent less than 1% of the total transforms identified by MMPA. A great wealth of design ideas remains effectively untapped and underutilized as the lack of measured data hinders extraction of robust trends. In this study we report the use of a quantitative structure-activity relationship (QSAR) model augmented MMPA approach (MMPA-by-QSAR) to infer the overall effect of chemical transformations on two essential ADME endpoints-lipophilicity and metabolic clearance. First, QSAR models are employed to predict compound activities, and subsequently, MMPA is used to identify and to extract virtual trends. Results obtained from retrospective analyses showed the ability to predict magnitudes of change close to experimental ones for the majority of transforms from each ADME data set. In the case of the lipophilicity endpoint (SFLogD) 73.7%, 87.85%, and 99% of transforms were predicted within 0.1, 0.15, and 0.3 units of the actual change. In the case of the clearance endpoint (HLM) 67.2%, 82.3%, and 99.5% of transforms were predicted within 0.08, 0.11, and 0.3 log units, respectively. Prospective application of MMPA-by-QSAR on untested compounds identified several novel transforms not observed in our measured data sets. When MMPs from these transforms were screened in our internal assays, it was found that the correct directionality of change was predicted for all but one of the tested transforms, and the predicted magnitudes of change have varying errors between predicted and measured mean changes ranging from 0.01 to 0.24 units for SFLogD and from 0.0 to 0.38 log units for HLM. This proposed MMPA-by-QSAR modeling approach has the potential to allow exploration of infrequent transforms or even identify completely novel transforms where no measured MMP is available.

A multi-endpoint matched molecular pair (MMP) analysis of 6-membered heterocycles.

Aromatic rings, ubiquitous in pharmaceutical compounds, are often exchanged with another ring during the optimization process of drug discovery. Inevitably, the preferred ring system for one endpoint may prove detrimental to another, thus necessitating a holistic, multiple endpoint optimization approach for finding the ideal replacement. Accordingly, we conducted an extensive matched molecular pair (MMP) analysis of common 6-membered aromatic rings across 4 endpoints critical for drug discovery (logD lipophilicity, microsomal metabolism, P-gp efflux and passive permeability). We also investigated the effect of context by considering the connecting atom. Heat maps were created as a simple yet comprehensive way to view and analyze the vast amount of interrelated data. Paired difference statistical tests were used to identify transforms with changes that were significantly different from zero. We conclude that the heat maps of transforms provide a unique and powerful approach for multiparameter optimization.

Interpretable, probability-based confidence metric for continuous quantitative structure-activity relationship models.

A great deal of research has gone into the development of robust confidence in prediction and applicability domain (AD) measures for quantitative structure-activity relationship (QSAR) models in recent years. Much of the attention has historically focused on structural similarity, which can be defined in many forms and flavors. A concept that is frequently overlooked in the realm of the QSAR applicability domain is how the local activity landscape plays a role in how accurate a prediction is or is not. In this work, we describe an approach that pairs information about both the chemical similarity and activity landscape of a test compound's neighborhood into a single calculated confidence value. We also present an approach for converting this value into an interpretable confidence metric that has a simple and informative meaning across data sets. The approach will be introduced to the reader in the context of models built upon four diverse literature data sets. The steps we will outline include the definition of similarity used to determine nearest neighbors (NN), how we incorporate the NN activity landscape with a similarity-weighted root-mean-square distance (wRMSD) value, and how that value is then calibrated to generate an intuitive confidence metric for prospective application. Finally, we will illustrate the prospective performance of the approach on five proprietary models whose predictions and confidence metrics have been tracked for more than a year.

Extraction of tacit knowledge from large ADME data sets via pairwise analysis.

Pharmaceutical companies routinely collect data across multiple projects for common ADME endpoints. Although at the time of collection the data is intended for use in decision making within a specific project, knowledge can be gained by data mining the entire cross-project data set for patterns of structure-activity relationships (SAR) that may be applied to any project. One such data mining method is pairwise analysis. This method has the advantage of being able to identify small structural changes that lead to significant changes in activity. In this paper, we describe the process for full pairwise analysis of our high-throughput ADME assays routinely used for compound discovery efforts at Pfizer (microsomal clearance, passive membrane permeability, P-gp efflux, and lipophilicity). We also describe multiple strategies for the application of these transforms in a prospective manner during compound design. Finally, a detailed analysis of the activity patterns in pairs of compounds that share the same molecular transformation reveals multiple types of transforms from an SAR perspective. These include bioisosteres, additives, multiplicatives, and a type we call switches as they act to either turn on or turn off an activity.

The use of matched molecular series networks for cross target structure activity relationship translation and potency prediction.

Matched molecular series (MMS) analysis is an extension of matched molecular pair (MMP) analysis where all of the MMPs belong to the same chemical series. An MMS within a biological assay is able to capture specific structure activity relationships resulting from chemical substitution at a single location in the molecule. Under this convention, an MMS has the ability to capture one specific interaction vector between the compounds in a series and their therapeutic target. MMS analysis has the potential to translate the SAR from one series to another even across different protein targets or assays. A significant limitation of this approach is the lack of chemical series with a sufficient number of overlapping fragments to establish a statistically strong SAR in most databases. This results in either an inability to perform MMS analysis altogether or a potentially high proportion of spurious matches from chance correlations when the MMS compound count is low. This paper presents the novel concept of an MMS Network, which captures the SAR relationships between a set of related MMSs and significantly enhances the performance of MMS analysis by reducing the number of spurious matches leading to the identification of unexpected and potentially transferable SAR across assays. The results of a full retrospective leave-one-out analysis and randomization simulation are provided, and examples of pharmaceutically relevant programs will be presented to demonstrate the potential of this method.

In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development.

In silico tools to investigate absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK) properties of new chemical entities are an integral part of the current industrial drug discovery paradigm. While many companies are active in the field, scientists engaged in this area do not necessarily share the same background and have limited resources when seeking guidance on how to initiate and maintain an in silico ADME-PK infrastructure in an industrial setting. This work summarizes the views of a group of industrial in silico and experimental ADME scientists, participating in the In Silico ADME Working Group, a subgroup of the International Consortium for Innovation through Quality in Pharmaceutical Development (IQ) Drug Metabolism Leadership Group. This overview on the benefits, caveats, and impact of in silico ADME-PK should serve as a resource for medicinal chemists, computational chemists, and DMPK scientists working in drug design to increase their knowledge in the area.

Evaluating the differences in cycloalkyl ether metabolism using the design parameter "lipophilic metabolism efficiency" (LipMetE) and a matched molecular pairs analysis.

We have observed previously that modification of ring size and substitution pattern may be used as a strategy to mitigate the metabolic instability of cycloalkyl ethers. In this article, we introduce a medicinal chemistry design parameter named "lipophilic metabolism efficiency" (LipMetE) that indicates that these changes in metabolic stability can be largely ascribed to changes in lipophilicity. Our matched molecular pair analysis also indicates that this finding is a general phenomenon, widely observed across different chemotypes. It is our hope that both the LipMetE design parameter and the results from our pairwise analysis will be useful tools for medicinal chemists.

Systematic and pairwise analysis of the effects of aromatic halogenation and trifluoromethyl substitution on human liver microsomal clearance.

Fluorine- and chlorine-containing moieties have been strategically integrated into chemical structures to optimize the pharmacokinetic and metabolic properties of therapeutic agents, based partly on the concept that the addition of these substituents may lower microsomal clearance. A large-scale systematic mechanistic study of drug metabolic alteration by aromatic halogenation has hitherto not been possible due to the lack of either large clearance databases or adequate data mining tools. To address this, we systematically searched compound pairs in Pfizer's human liver microsomal clearance database of over 220,000 unique compounds to assess the effects of fluoro-, chloro- and trifluoromethyl-substitution on phenyl derivatives. Although the para-position fluorination and chlorination lowered the microsomal clearance statistically, the substitution at the ortho and meta positions for the studied fluorine- and chlorine-containing moieties dramatically increased the microsomal clearance. More importantly, we found that changes in physicochemical properties, electronic properties, and specific binding of substrates to drug metabolizing enzymes, for instance, cytochrome P450s, are all determining factors that drive the direction of microsomal clearance when a specific series of compounds are studied.