IMHB-Mediated Chameleonicity in Drug Design: A Focus on Structurally Related PROTACs

Molecular chameleonicity may enable compounds to compensate for the unfavorable ADME properties typically associated with complex molecules, such as PROTACs. Here we present a few in silico strategies to implement chameleonicity considerations in drug design. Initially, we identified six structurally related CRBN-based PROTACs targeting BET proteins and experimentally verified whether chameleonicity is needed to obtain an acceptable physicochemical profile. Then, we utilized experimental data to validate our novel computational strategies based on tools crafted to encompass a spectrum of complexities and innovative features. After confirming that the formation of IMHBs is the primary driving factor behind chameleonicity, we initially utilized conformational sampling data to define cChameCS, an IMHB-mediated, simple, and rapid chameleonicity predictor index suitable for early drug discovery. Subsequently, we identified dynamic IMHB patterns relevant to chameleonicity through molecular dynamics simulations. Finally, we proposed a workflow for designing structurally related chameleonic PROTACs of potential application in the lead optimization process.

Beyond Rule of Five and PROTACs in Modern Drug Discovery: Polarity Reducers, Chameleonicity, and the Evolving Physicochemical Landscape

Developing orally bioavailable drugs demands an understanding of absorption in early drug development. Traditional methods and physicochemical properties optimize absorption for rule of five (Ro5) compounds; beyond rule of five (bRo5) drugs necessitate advanced tools like the experimental measure of exposed polarity (EPSA) and the AbbVie multiparametric score (AB-MPS). Analyzing AB-MPS and EPSA against ∼1000 compounds with human absorption data and ∼10,000 AbbVie tool compounds (∼1000 proteolysis targeting chimeras or PROTACs, ∼7000 Ro5s, and ∼2000 bRo5s) revealed new patterns of physicochemical trends. We introduced a high-throughput "polarity reduction" descriptor: ETR, the EPSA-to-topological polar surface area (TPSA) ratio, highlights unique bRo5 and PROTAC subsets for specialized drug design strategies for effective absorption. Our methods and guidelines refine drug design by providing innovative in vitro approaches, enhancing physicochemical property optimization, and enabling accurate predictions of intestinal absorption in the complex bRo5 domain.

Exploring the chemical space of orally bioavailable PROTACs

A principal challenge in the discovery of proteolysis targeting chimeras (PROTACs) as oral medications is their bioavailability. To facilitate drug design, it is therefore essential to identify the chemical space where orally bioavailable PROTACs are more likely to be situated. To this aim, we extracted structure-bioavailability insights from published data using traditional 2D descriptors, thereby shedding light on their potential and limitations as drug design tools. Subsequently, we describe cutting-edge experimental, computational and hybrid design strategies based on 3D descriptors, which show promise for enhancing the probability of discovering PROTACs with high oral bioavailability.

A New Version of the Tissue Composition-Based Model for Improving the Mechanism-Based Prediction of Volume of Distribution at Steady-State for Neutral Drugs

In-vitro models are available in the literature for predicting the volume of distribution at steady-state (Vdss) of drugs. The mechanistic model refers to the tissue composition-based model (TCM), which includes important factors that govern Vdss such as drug physiochemistry and physiological data. The recognized TCM published by Rodgers and Rowland (TCM-RR) and a subsequent adjustment made by Simulations Plus Inc. (TCM-SP) have been shown to be generally less accurate with neutral compared to ionized drugs. Therefore, improving these models for neutral drugs becomes necessary. The objective of this study was to propose a new TCM for improving the prediction of Vdss for neutral drugs. The new TCM included two modifications of the published models (i) accentuate the effect of the blood-to-plasma ratio (BPR) that should cover permeated molecules across the biomembranes, which is lacking in these models for neutral compounds, and (ii) use a different approach to estimate the binding in tissues. The new TCM was validated with a large dataset of 202 commercial and proprietary compounds including preclinical and clinical data. All scenario datasets were predicted more accurately with the TCM-New, whereas all statistical parameters indicate that the TCM-New showed significant improvements in terms of accuracy over the TCM-RR and TCM-SP. Predictions of Vdss were frequently more accurate for the TCM-new with 83% within twofold error versus only 50% for the TCM-RR. And more than 95% of the predictions were within threefold error and patient interindividual differences can be predicted with the TCM-New, greatly exceeding the accuracy of the published models. Overall, the new TCM incorporating BPR significantly improved the Vdss predictions in animals and humans for neutral drugs, and, hence, has the potential to better support the drug discovery and facilitate the first-in-human predictions.

Rapid PROTAC Discovery Platform: Nanomole-Scale Array Synthesis and Direct Screening of Reaction Mixtures

Precise length, shape, and linker attachment points are all integral components to designing efficacious proteolysis targeting chimeras (PROTACs). Due to the synthetic complexity of these heterobifunctional degraders and the difficulty of computational modeling to aid PROTAC design, the exploration of structure-activity relationships remains mostly empirical, which requires a significant investment of time and resources. To facilitate rapid hit finding, we developed capabilities for PROTAC parallel synthesis and purification by harnessing an array of preformed E3-ligand-linker intermediates. In the next iteration of this approach, we developed a rapid, nanomole-scale PROTAC synthesis methodology using amide coupling that enables direct screening of nonpurified reaction mixtures in cell-based degradation assays, as well as logD and EPSA measurements. This approach greatly expands and accelerates PROTAC SAR exploration (5 days instead of several weeks) as well as avoids laborious and solvent-demanding purification of the reaction mixtures, thus making it an economical and more sustainable methodology for PROTAC hit finding.

Chamelogk: A Chromatographic Chameleonicity Quantifier to Design Orally Bioavailable Beyond-Rule-of-5 Drugs

New chemical modalities in drug discovery include molecules belonging to the bRo5 chemical space. Because of their complex and flexible structure, bRo5 compounds often suffer from a poor solubility/permeability profile. Chameleonicity describes the capacity of a molecule to adapt to the environment through conformational changes; the design of molecular chameleons is a medicinal chemistry strategy simultaneously optimizing solubility and permeability. A default method to quantify chameleonicity in early drug discovery is still missing. Here we introduce Chamelogk, an automated, fast, and cheap chromatographic descriptor of chameleonicity. Moreover, we report measurements for 55 Ro5 and bRo5 compounds and validate our method with literature data. Then, selected case studies (macrocycles, nonmacrocyclic compounds, and PROTACs) are used to illustrate the application of Chamelogk in combination with lipophilicity (BRlogD) and polarity (Δ log kwIAM) descriptors. Overall, we show how Chamelogk deserves being included in property-based drug discovery strategies to design oral bioavailable bRo5 compounds.

Today's drug discovery and the shadow of the rule of 5

INTRODUCTION

The rule of 5 developed by Lipinski et al., a landmark and prescient piece of scholarship, focused the minds of drug hunters by systematically characterizing the physical make-up of drug molecules for the first time, noting many sub-optimal compounds identified by high-throughput screening practices. Its profound influence on thinking and practices, whilst providing benefit, perhaps etched the guidelines too strongly in the minds of some drug hunters who applied the bounds too literally without understanding the implications of the underlying statistics.

AREAS COVERED

This opinion is based on recent key developments that take thinking, measurements, and standards beyond those first set out, particularly the influences of molecular weight and the understanding, measurement, and calculation of lipophilicity.

EXPERT OPINION

Techniques and technologies for physicochemical estimations set new standards. It is timely to celebrate the significance and influence of the rule of 5, whilst taking thinking to new levels with better characterizations. The shadow of the rule of 5 may be long, but it is not dark, as new measurements, predictions and principles emerge as guiding lights in the design and prioritization of higher-quality molecules redefining the meaning of beyond the rule of 5.

Advancing Strategies for Proteolysis-Targeting Chimera Design

Proteolysis-targeting chimeras (PROTACs) have shown great therapeutic potential by degrading various disease-causing proteins, particularly those related to tumors. Therefore, the introduction of PROTACs has ushered in a new chapter of antitumor drug development, marked by significant advances over recent years. Herein, we describe recent developments in PROTAC technology, focusing on design strategy, development workflow, and future outlooks. We also discuss potential opportunities and challenges for PROTAC research.

Conformational Sampling Deciphers the Chameleonic Properties of a VHL-Based Degrader

Chameleonicity (the capacity of a molecule to adapt its conformations to the environment) may help to identify orally bioavailable drugs in the beyond-Rule-of-5 chemical space. Computational methods to predict the chameleonic behaviour of degraders have not yet been reported and the identification of molecular chameleons still relies on experimental evidence. Therefore, there is a need to tune predictions with experimental data. Here, we employ PROTAC-1 (a passively cell-permeable degrader), for which NMR and physicochemical data prove the chameleonic behaviour, to benchmark the capacity of two conformational sampling algorithms and selection schemes. To characterize the conformational ensembles in both polar and nonpolar environments, we compute three molecular properties proven to be essential for cell permeability: conformer shape (radius of gyration), polarity (3D PSA), and the number of intramolecular hydrogen bonds. Energetic criteria were also considered. Infographics monitored the simultaneous variation of those properties in computed and NMR conformers. Overall, we provide key points for tuning conformational sampling tools to reproduce PROTAC-1 chameleonicity according to NMR evidence. This study is expected to improve the design of PROTAC drugs and the development of computational sustainable strategies to exploit the potential of new modalities in drug discovery.

A Narrative Review of the Impact of Extracorporeal Membrane Oxygenation on the Pharmacokinetics and Pharmacodynamics of Critical Care Therapies

Objective:

Extracorporeal membrane oxygenation (ECMO) utilization is increasing on a global scale, and despite technological advances, minimal standardized approaches to pharmacotherapeutic management exist. This objective was to create a comprehensive review for medication dosing in ECMO based on the most current evidence.

Data Sources:

A literature search of PubMed was performed for all pertinent articles prior to 2022. The following search terms were utilized: ECMO, pharmacokinetics, pharmacodynamics, sedation, analgesia, antiepileptic, anticoagulation, antimicrobial, antifungal, nutrition. Retrospective cohort studies, case-control studies, case series, case reports, and ex vivo investigations were reviewed.

Study Selection and Data Extraction:

PubMed (1975 through July 2022) was the database used in the literature search. Non-English studies were excluded. Search terms included both drug class categories, specific drug names, ECMO, and pharmacokinetics.

Data Synthesis:

Medications with high protein binding (>70%) and high lipophilicity (logP > 2) are associated with circuit sequestration and the potential need for dose adjustment. Volume of distribution changes with ECMO may also impact dosing requirements of common critical care medications. Lighter sedation targets and analgosedation may help reduce sedative and analgesia requirements, whereas higher antiepileptic dosing is recommended. Vancomycin is minimally affected by the ECMO circuit and recommendations for dosing in critically ill adults are reasonable. Anticoagulation remains challenging as optimal aPTT goals have not been established.

Relevance to Patient Care and Clinical Practice:

This review describes the anticipated impacts of ECMO circuitry on sedatives, analgesics, anticoagulation, antiepileptics, antimicrobials, antifungals, and nutrition support and provides recommendations for drug therapy management.

Conclusions:

Medication pharmacokinetic/pharmacodynamic parameters should be considered when determining the potential impact of the ECMO circuit on attainment of therapeutic effect and target serum drug concentrations, and should guide therapy choices and/or dose adjustments when data are not available.

Refinement of Computational Access to Molecular Physicochemical Properties: From Ro5 to bRo5

There is a need of computational tools to rank bRo5 drug candidates in the very early phases of drug discovery when chemical matter is unavailable. In this study, we selected three compounds: (a) a Ro5 drug (Pomalidomide), (b) a bRo5 orally available drug (Saquinavir), and (c) a polar PROTAC (CMP 98) to focus on computational access to physicochemical properties. To provide a benchmark, the three compounds were first experimentally characterized for their lipophilicity, polarity, IMHBs, and chameleonicity. To reproduce the experimental information content, we generated conformer ensembles with conformational sampling and molecular dynamics in both water and nonpolar solvents. Then we calculated Rgyr, 3D PSA, and IMHB number. An innovative pool of strategies for data analysis was then provided. Overall, we report a contribution to close the gap between experimental and computational methods for characterizing bRo5 physicochemical properties.

Designing Soluble PROTACs: Strategies and Preliminary Guidelines

Solubility optimization is a crucial step to obtaining oral PROTACs. Here we measured the thermodynamic solubilities (log S) of 21 commercial PROTACs. Next, we measured BRlogD and log k_w^IAM (lipophilicity), EPSA, and Δ log k_w^IAM (polarity) and showed that lipophilicity plays a major role in governing log S, but a contribution of polarity cannot be neglected. Two-/three-dimensional descriptors calculated on conformers arising from conformational sampling and steered molecular dynamics failed in modeling solubility. Infographic tools were used to identify a privileged region of soluble PROTACs in a chemical space defined by BRlogD, log k_w^IAM and topological polar surface area, while machine learning provided a log S classification model. Finally, for three pairs of PROTACs we measured the solubility, lipophilicity, and polarity of the building blocks and identified the limits of estimating PROTAC solubility from the synthetic components. Overall, this paper provides promising guidelines for optimizing PROTAC solubility in early drug discovery programs.

Predicting Solubility of Newly-Approved Drugs (2016–2020) with a Simple ABSOLV and GSE(Flexible-Acceptor) Consensus Model Outperforming Random Forest Regression

This study applies the ‘Flexible-Acceptor’ variant of the General Solubility Equation, GSE(Φ,B), to the prediction of the aqueous intrinsic solubility, log₁₀S₀, of FDA recently-approved (2016–2020) ‘small-molecule’ new molecular entities (NMEs). The novel equation had been shown to predict the solubility of drugs beyond Lipinski’s ‘Rule of 5’ chemical space (bRo5) to a precision nearly matching that of the Random Forest Regression (RFR) machine learning method. Since then, it was found that the GSE(Φ,B) appears to work well not only for bRo5 NMEs, but also for Ro5 drugs. To put context to GSE(Φ,B), Yalkowsky’s GSE(classic), Abraham’s ABSOLV, and Breiman’s RFR models were also applied to predict log₁₀ S₀ of 72 newly-approve NMEs, for which useable reported solubility values could be accessed (nearly 60% from FDA New Drug Application published reports). Except for GSE (classic), the prediction models were retrained with an enlarged version of the Wiki-pS₀ database (nearly 400 added log₁₀ S₀ entries since our recent previous study). Thus, these four models were further validated by the additional independent solubility measurements which the newly-approved drugs introduced. The prediction methods ranked RFR ~ GSE (Φ,B) > ABSOLV > GSE (classic) in performance. It was further demonstrated that the biases generated in the four separate models could be nearly eliminated in a consensus model based on the average of just two of the methods: GSE (Φ,B) and ABSOLV. The resulting consensus prediction equation is simple in form and can be easily incorporated into spreadsheet calculations. Even more significant, it slightly outperformed the RFR method.

Global Analysis of Models for Predicting Human Absorption: QSAR, In Vitro, and Preclinical Models

Models intended to predict intestinal absorption are an essential part of the drug development process. Although many models exist for capturing intestinal absorption, many questions still exist around the applicability of these models to drug types like "beyond rule of 5" (bRo5) and low absorption compounds. This presents a challenge as current models have not been rigorously tested to understand intestinal absorption. Here, we assembled a large, structurally diverse dataset of ∼1000 compounds with known in vitro, preclinical, and human permeability and/or absorption data. In silico (quantitative structure-activity relationship), in vitro (Caco-2), and in vivo (rat) models were statistically evaluated for predictive performance against this human intestinal absorption dataset. We expect this evaluation to serve as a resource for DMPK scientists and medicinal/computational chemists to increase their understanding of permeability and absorption model utility and applications for academia and industry.

Characterization of the new CelerisTM Arginine column: Retentive behaviour through a combination of chemometric tools and potential in drug analysis

Celeris^TM Arginine (ARG) is a mixed-mode stationary phase recently released on the market. To characterize its analytical behavior, the retention factors of a pool (n=100, of which 36 neutrals, 26 acids and 38 bases) of pharmaceutically relevant compounds have been measured on this phase over eight percentages (from 10 to 90% v/v) of acetonitrile (MeCN) as organic modifier. The ARG phase exhibited enhanced affinity for the molecules that are in their anionic form at the experimental pH, whilst basic compounds, albeit over a wide range of lipophilicity and pK_a values, were on average poorly retained. To dissect the separation mechanism of the ARG phase, the overall analytical retention has been deconvoluted into the individual contributions of intermolecular forces by a QSPR/ Partial Least Square (PLS)/Block Relevance (BR) analysis tool recently developed by us. For the neutrals, the most relevant blocks were found to be Size, describing the interaction due to the dimension of the molecule, and O, representing the solute's hydrogen bond donor properties. The change in sign from positive to negative of the Size block, which occurs between 10% and 20% MeCN, allowed to visually appreciate the switch in the separation mode from reversed phase to normal phase. Some good statistic models for rationalizing the analytical behaviour of neutrals were developed from VS+ descriptors. However, their performance in modelling the analytical retention of acids was substandard, probably due to the intrinsic inefficacy of VS+ descriptors in handling electric charges. This instance was addressed by a complimentary MLR strategy, which led to successfully model the retention of acids on the ARG column and to shed light into their retention mechanism, which seemed to be substantially driven by electrostatics.

"Flexible-Acceptor" General Solubility Equation for beyond Rule of 5 Drugs

This study describes a novel nonlinear variant of the well-known Yalkowsky general solubility equation (GSE). The modified equation can be trained with small molecules, mostly from the Lipinski Rule of 5 (Ro5) chemical space, to predict the intrinsic aqueous solubility, S₀, of large molecules (MW > 800 Da) from beyond the rule of 5 (bRo5) space, to an accuracy almost equal to that of a recently described random forest regression (RFR) machine learning analysis. The new approach replaces the GSE constant factors in the intercept (0.5), the octanol-water log P (-1.0), and melting point, mp (-0.01) terms with simple exponential functions incorporating the sum descriptor, Φ+B (Kier Φ molecular flexibility and Abraham H-bond acceptor potential). The constants in the modified three-variable (log P, mp, Φ+B) equation were determined by partial least-squares (PLS) refinement using a small-molecule log S₀ training set (n = 6541) of mostly druglike molecules. In this "flexible-acceptor" GSE(Φ,B) model, the coefficient of log P (normally fixed at -1.0) varies smoothly from -1.1 for rigid nonionizable molecules (Φ+B = 0) to -0.39 for typically flexible (Φ ∼ 20, B ∼ 6) large molecules. The intercept (traditionally fixed at +0.5) varies smoothly from +1.9 for completely inflexible small molecules to -2.2 for typically flexible large molecules. The mp coefficient (-0.007) remains practically constant, near the traditional value (-0.01) for most molecules, which suggests that the small-to-large molecule continuum is mainly solvation responsive, apparently with only minor changes in the crystal lattice contributions. For a test set of 32 large molecules (e.g., cyclosporine A, gramicidin A, leuprolide, nafarelin, oxytocin, vancomycin, and mostly natural-product-derived therapeutics used in infectious/viral diseases, in immunosuppression, and in oncology) the modified equation predicted the intrinsic solubility with a root-mean-square error of 1.10 log unit, compared to 3.0 by the traditional GSE, and 1.07 by RFR.

Updating the portfolio of physicochemical descriptors related to permeability in the beyond the rule of 5 chemical space

Beyond rule of 5 (bRo5) molecules are attracting significant interest in modern drug discovery mostly because many novel targets require large and more flexible structures. The main aim of this paper is the identification of ad hoc bRo5 physicochemical descriptors of ionization, lipophilicity, polarity and chameleonicity and their measurement. We used different methods to collect ionization (pK_a measures and log k'80 PLRP-S trends), lipophilicity (in octanol/water, in apolar systems and in biomimetic environments), polarity (Δlog P_oct-tol, EPSA and Δlog K_W^IAM) and chameleonicity (ChameLogD) descriptors for 26 bRo5 drugs. A second aim was to check the relationship between physicochemical descriptors and permeability for a subset of compounds for which solid permeability values are reported in the literature. Results showed that the physicochemical profile in the bRo5 chemical space is often experimentally accessible, albeit more tools are required to overcome limitations of individual methods. For the investigated compounds, permeability is governed by Δlog P_oct-tol and preliminary data support that chameleonicity could also have an impact.

Flexibility in early drug discovery: focus on the beyond-Rule-of-5 chemical space

Large and flexible compounds are of interest in pharmaceutical programs aimed at challenging protein targets that cannot be modulated by Rule of Five (Ro5)-compliant small molecules. Given their particular structural features, early drug discovery is now in charge of identifying which molecular descriptors should be used in the often called beyond-Rule-of-5 (bRo5) chemical space. Here, we focus on flexibility descriptors. First, we discuss the concept of flexibility and then focus on the number of rotatable bonds (NRot), the most common in silico descriptor. After identifying the pros and cons of NRot, we discuss how Kier's index Φ can replace NRot, and the limits of 3D descriptors. Finally, we show how a misuse of NRot and Φ can result in incorrect interpretations of the impact of flexibility in the bRo5 space and how flexibility has potential in the prospective design of orally bioavailable bRo5 drug candidates.