Developing Covalent Protein Drugs via Proximity-Enabled Reactive Therapeutics.

Small molecule covalent drugs provide desirable therapeutic properties over noncovalent ones for treating challenging diseases. The potential of covalent protein drugs, however, remains unexplored due to protein's inability to bind targets covalently. We report a proximity-enabled reactive therapeutics (PERx) approach to generate covalent protein drugs. Through genetic code expansion, a latent bioreactive amino acid fluorosulfate-L-tyrosine (FSY) was incorporated into human programmed cell death protein-1 (PD-1). Only when PD-1 interacts with PD-L1 did the FSY react with a proximal histidine of PD-L1 selectively, enabling irreversible binding of PD-1 to only PD-L1 in vitro and in vivo. When administrated in immune-humanized mice, the covalent PD-1(FSY) exhibited strikingly more potent antitumor effect over the noncovalent wild-type PD-1, attaining therapeutic efficacy equivalent or superior to anti-PD-L1 antibody. PERx should provide a general platform technology for converting various interacting proteins into covalent binders, achieving specific covalent protein targeting for biological studies and therapeutic capability unattainable with conventional noncovalent protein drugs.

Personalized Mapping of Drug Metabolism by the Human Gut Microbiome.

The human gut microbiome harbors hundreds of bacterial species with diverse biochemical capabilities. Dozens of drugs have been shown to be metabolized by single isolates from the gut microbiome, but the extent of this phenomenon is rarely explored in the context of microbial communities. Here, we develop a quantitative experimental framework for mapping the ability of the human gut microbiome to metabolize small molecule drugs: Microbiome-Derived Metabolism (MDM)-Screen. Included are a batch culturing system for sustained growth of subject-specific gut microbial communities, an ex vivo drug metabolism screen, and targeted and untargeted functional metagenomic screens to identify microbiome-encoded genes responsible for specific metabolic events. Our framework identifies novel drug-microbiome interactions that vary between individuals and demonstrates how the gut microbiome might be used in drug development and personalized medicine.

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

Pharmaceutical-Grade Rigosertib Is a Microtubule-Destabilizing Agent.

We recently used CRISPRi/a-based chemical-genetic screens and cell biological, biochemical, and structural assays to determine that rigosertib, an anti-cancer agent in phase III clinical trials, kills cancer cells by destabilizing microtubules. Reddy and co-workers (Baker et al., 2020, this issue of Molecular Cell) suggest that a contaminating degradation product in commercial formulations of rigosertib is responsible for the microtubule-destabilizing activity. Here, we demonstrate that cells treated with pharmaceutical-grade rigosertib (>99.9% purity) or commercially obtained rigosertib have qualitatively indistinguishable phenotypes across multiple assays. The two formulations have indistinguishable chemical-genetic interactions with genes that modulate microtubule stability, both destabilize microtubules in cells and in vitro, and expression of a rationally designed tubulin mutant with a mutation in the rigosertib binding site (L240F TUBB) allows cells to proliferate in the presence of either formulation. Importantly, the specificity of the L240F TUBB mutant for microtubule-destabilizing agents has been confirmed independently. Thus, rigosertib kills cancer cells by destabilizing microtubules, in agreement with our original findings.

Copper-mediated synthesis of drug-like bicyclopentanes.

Multicomponent reactions are relied on in both academic and industrial synthetic organic chemistry owing to their step- and atom-economy advantages over traditional synthetic sequences1. Recently, bicyclo[1.1.1]pentane (BCP) motifs have become valuable as pharmaceutical bioisosteres of benzene rings, and in particular 1,3-disubstituted BCP moieties have become widely adopted in medicinal chemistry as para-phenyl ring replacements2. These structures are often generated from [1.1.1]propellane via opening of the internal C-C bond through the addition of either radicals or metal-based nucleophiles3-13. The resulting propellane-addition adducts are then transformed to the requisite polysubstituted BCP compounds via a range of synthetic sequences that traditionally involve multiple chemical steps. Although this approach has been effective so far, a multicomponent reaction that enables single-step access to complex and diverse polysubstituted drug-like BCP products would be more time efficient compared to current stepwise approaches. Here we report a one-step three-component radical coupling of [1.1.1]propellane to afford diverse functionalized bicyclopentanes using various radical precursors and heteroatom nucleophiles via a metallaphotoredox catalysis protocol. This copper-mediated reaction operates on short timescales (five minutes to one hour) across multiple (more than ten) nucleophile classes and can accommodate a diverse array of radical precursors, including those that generate alkyl, α-acyl, trifluoromethyl and sulfonyl radicals. This method has been used to rapidly prepare BCP analogues of known pharmaceuticals, one of which is substantially more metabolically stable than its commercial progenitor.

INTEDE 2.0: the metabolic roadmap of drugs.

The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter.

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

A small-molecule drug inhibits autophagy gene expression through the central regulator TFEB.

Autophagy supports the fast growth of established tumors and promotes tumor resistance to multiple treatments. Inhibition of autophagy is a promising strategy for tumor therapy. However, effective autophagy inhibitors suitable for clinical use are currently lacking. There is a high demand for identifying novel autophagy drug targets and potent inhibitors with drug-like properties. The transcription factor EB (TFEB) is the central transcriptional regulator of autophagy, which promotes lysosomal biogenesis and functions and systematically up-regulates autophagy. Despite extensive evidence that TFEB is a promising target for autophagy inhibition, no small molecular TFEB inhibitors were reported. Here, we show that an United States Food and Drug Administration (FDA)-approved drug Eltrombopag (EO) binds to the basic helix-loop-helix-leucine zipper domain of TFEB, specifically the bottom surface of helix-loop-helix to clash with DNA recognition, and disrupts TFEB-DNA interaction in vitro and in cellular context. EO selectively inhibits TFEB's transcriptional activity at the genomic scale according to RNA sequencing analyses, blocks autophagy in a dose-dependent manner, and increases the sensitivity of glioblastoma to temozolomide in vivo. Together, this work reveals that TFEB is targetable and presents the first direct TFEB inhibitor EO, a drug compound with great potential to benefit a wide range of cancer therapies by inhibiting autophagy.

Time to Change: A Systems Pharmacology Approach to Disentangle Mechanisms of Drug-Induced Mitochondrial Toxicity.

An increasing number of commonly prescribed drugs are known to interfere with mitochondrial function, which is associated with almost half of all Food and Drug Administration black box warnings, a variety of drug withdrawals, and attrition of drug candidates. This can mainly be attributed to a historic lack of sensitive and specific assays to identify the mechanisms underlying mitochondrial toxicity during drug development. In the last decade, a better understanding of drug-induced mitochondrial dysfunction has been achieved by network-based and structure-based systems pharmacological approaches. Here, we propose the implementation of a tiered systems pharmacology approach to detect adverse mitochondrial drug effects during preclinical drug development, which is based on a toolset developed to study inherited mitochondrial disease. This includes phenotypic characterization, profiling of key metabolic alterations, mechanistic studies, and functional in vitro and in vivo studies. Combined with binding pocket similarity comparisons and bottom-up as well as top-down metabolic network modeling, this tiered approach enables identification of mechanisms underlying drug-induced mitochondrial dysfunction. After validation of these off-target mechanisms, drug candidates can be adjusted to minimize mitochondrial activity. Implementing such a tiered systems pharmacology approach could lead to a more efficient drug development trajectory due to lower drug attrition rates and ultimately contribute to the development of safer drugs. SIGNIFICANCE STATEMENT: Many commonly prescribed drugs adversely affect mitochondrial function, which can be detected using phenotypic assays. However, these methods provide only limited insight into the underlying mechanisms. In recent years, a better understanding of drug-induced mitochondrial dysfunction has been achieved by network-based and structure-based system pharmacological approaches. Their implementation in preclinical drug development could reduce the number of drug failures, contributing to safer drug design.

BindingSiteDTI: differential-scale binding site modelling for drug-target interaction prediction.

MOTIVATION: Enhanced by contemporary computational advances, the prediction of drug-target interactions (DTIs) has become crucial in developing de novo and effective drugs. Existing deep learning approaches to DTI prediction are frequently beleaguered by a tendency to overfit specific molecular representations, which significantly impedes their predictive reliability and utility in novel drug discovery contexts. Furthermore, existing DTI networks often disregard the molecular size variance between macro molecules (targets) and micro molecules (drugs) by treating them at an equivalent scale that undermines the accurate elucidation of their interaction. RESULTS: We propose a novel DTI network with a differential-scale scheme to model the binding site for enhancing DTI prediction, which is named as BindingSiteDTI. It explicitly extracts multiscale substructures from targets with different scales of molecular size and fixed-scale substructures from drugs, facilitating the identification of structurally similar substructural tokens, and models the concealed relationships at the substructural level to construct interaction feature. Experiments conducted on popular benchmarks, including DUD-E, human, and BindingDB, shown that BindingSiteDTI contains significant improvements compared with recent DTI prediction methods. AVAILABILITY AND IMPLEMENTATION: The source code of BindingSiteDTI can be accessed at https://github.com/MagicPF/BindingSiteDTI.

FMCA-DTI: a fragment-oriented method based on a multihead cross attention mechanism to improve drug-target interaction prediction.

MOTIVATION: Identifying drug-target interactions (DTI) is crucial in drug discovery. Fragments are less complex and can accurately characterize local features, which is important in DTI prediction. Recently, deep learning (DL)-based methods predict DTI more efficiently. However, two challenges remain in existing DL-based methods: (i) some methods directly encode drugs and proteins into integers, ignoring the substructure representation; (ii) some methods learn the features of the drugs and proteins separately instead of considering their interactions. RESULTS: In this article, we propose a fragment-oriented method based on a multihead cross attention mechanism for predicting DTI, named FMCA-DTI. FMCA-DTI obtains multiple types of fragments of drugs and proteins by branch chain mining and category fragment mining. Importantly, FMCA-DTI utilizes the shared-weight-based multihead cross attention mechanism to learn the complex interaction features between different fragments. Experiments on three benchmark datasets show that FMCA-DTI achieves significantly improved performance by comparing it with four state-of-the-art baselines. AVAILABILITY AND IMPLEMENTATION: The code for this workflow is available at: https://github.com/jacky102022/FMCA-DTI.

Drug-target interaction predictions with multi-view similarity network fusion strategy and deep interactive attention mechanism.

MOTIVATION: Accurately identifying the drug-target interactions (DTIs) is one of the crucial steps in the drug discovery and drug repositioning process. Currently, many computational-based models have already been proposed for DTI prediction and achieved some significant improvement. However, these approaches pay little attention to fuse the multi-view similarity networks related to drugs and targets in an appropriate way. Besides, how to fully incorporate the known interaction relationships to accurately represent drugs and targets is not well investigated. Therefore, there is still a need to improve the accuracy of DTI prediction models. RESULTS: In this study, we propose a novel approach that employs Multi-view similarity network fusion strategy and deep Interactive attention mechanism to predict Drug-Target Interactions (MIDTI). First, MIDTI constructs multi-view similarity networks of drugs and targets with their diverse information and integrates these similarity networks effectively in an unsupervised manner. Then, MIDTI obtains the embeddings of drugs and targets from multi-type networks simultaneously. After that, MIDTI adopts the deep interactive attention mechanism to further learn their discriminative embeddings comprehensively with the known DTI relationships. Finally, we feed the learned representations of drugs and targets to the multilayer perceptron model and predict the underlying interactions. Extensive results indicate that MIDTI significantly outperforms other baseline methods on the DTI prediction task. The results of the ablation experiments also confirm the effectiveness of the attention mechanism in the multi-view similarity network fusion strategy and the deep interactive attention mechanism. AVAILABILITY AND IMPLEMENTATION: https://github.com/XuLew/MIDTI.

Revisiting drug-protein interaction prediction: a novel global-local perspective.

MOTIVATION: Accurate inference of potential drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance. RESULTS: We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multilayer perceptrons to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach is expected to offer valuable insights for furthering drug repurposing and personalized medicine research. AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.

Biosynthesis of natural and halogenated plant monoterpene indole alkaloids in yeast.

Monoterpenoid indole alkaloids (MIAs) represent a large class of plant natural products with marketed pharmaceutical activities against a wide range of indications, including cancer, malaria and hypertension. Halogenated MIAs have shown improved pharmaceutical properties; however, synthesis of new-to-nature halogenated MIAs remains a challenge. Here we demonstrate a platform for de novo biosynthesis of two MIAs, serpentine and alstonine, in baker's yeast Saccharomyces cerevisiae and deploy it to systematically explore the biocatalytic potential of refactored MIA pathways for the production of halogenated MIAs. From this, we demonstrate conversion of individual haloindole derivatives to a total of 19 different new-to-nature haloserpentine and haloalstonine analogs. Furthermore, by process optimization and heterologous expression of a modified halogenase in the microbial MIA platform, we document de novo halogenation and biosynthesis of chloroalstonine. Together, this study highlights a microbial platform for enzymatic exploration and production of complex natural and new-to-nature MIAs with therapeutic potential.

Validity conditions of approximations for a target-mediated drug disposition model: A novel first-order approximation and its comparison to other approximations.

Target-mediated drug disposition (TMDD) is a phenomenon characterized by a drug's high-affinity binding to a target molecule, which significantly influences its pharmacokinetic profile within an organism. The comprehensive TMDD model delineates this interaction, yet it may become overly complex and computationally demanding in the absence of specific concentration data for the target or its complexes. Consequently, simplified TMDD models employing quasi-steady state approximations (QSSAs) have been introduced; however, the precise conditions under which these models yield accurate results require further elucidation. Here, we establish the validity of three simplified TMDD models: the Michaelis-Menten model reduced with the standard QSSA (mTMDD), the QSS model reduced with the total QSSA (qTMDD), and a first-order approximation of the total QSSA (pTMDD). Specifically, we find that mTMDD is applicable only when initial drug concentrations substantially exceed total target concentrations, while qTMDD can be used for all drug concentrations. Notably, pTMDD offers a simpler and faster alternative to qTMDD, with broader applicability than mTMDD. These findings are confirmed with antibody-drug conjugate real-world data. Our findings provide a framework for selecting appropriate simplified TMDD models while ensuring accuracy, potentially enhancing drug development and facilitating safer, more personalized treatments.

Mapping human microbiome drug metabolism by gut bacteria and their genes.

Individuals vary widely in their responses to medicinal drugs, which can be dangerous and expensive owing to treatment delays and adverse effects. Although increasing evidence implicates the gut microbiome in this variability, the molecular mechanisms involved remain largely unknown. Here we show, by measuring the ability of 76 human gut bacteria from diverse clades to metabolize 271 orally administered drugs, that many drugs are chemically modified by microorganisms. We combined high-throughput genetic analyses with mass spectrometry to systematically identify microbial gene products that metabolize drugs. These microbiome-encoded enzymes can directly and substantially affect intestinal and systemic drug metabolism in mice, and can explain the drug-metabolizing activities of human gut bacteria and communities on the basis of their genomic contents. These causal links between the gene content and metabolic activities of the microbiota connect interpersonal variability in microbiomes to interpersonal differences in drug metabolism, which has implications for medical therapy and drug development across multiple disease indications.

Drugs Form Ternary Complexes with Human Liver Fatty Acid Binding Protein 1 (FABP1) and FABP1 Binding Alters Drug Metabolism.

Liver fatty acid binding protein 1 (FABP1) binds diverse endogenous lipids and is highly expressed in the human liver. Binding to FABP1 alters the metabolism and homeostasis of endogenous lipids in the liver. Drugs have also been shown to bind to rat FABP1, but limited data are available for human FABP1 (hFABP1). FABP1 has a large binding pocket, and up to two fatty acids can bind to FABP1 simultaneously. We hypothesized that drug binding to hFABP1 results in formation of ternary complexes and that FABP1 binding alters drug metabolism. To test these hypotheses, native protein mass spectrometry (MS) and fluorescent 11-(dansylamino)undecanoic acid (DAUDA) displacement assays were used to characterize drug binding to hFABP1, and diclofenac oxidation by cytochrome P450 2C9 (CYP2C9) was studied in the presence and absence of hFABP1. DAUDA binding to hFABP1 involved high (Kd,1 = 0.2 µM) and low (Kd,2 > 10 µM) affinity binding sites. Nine drugs bound to hFABP1 with equilibrium dissociation constant (Kd) values ranging from 1 to 20 µM. None of the tested drugs completely displaced DAUDA from hFABP1, and fluorescence spectra showed evidence of ternary complex formation. Formation of DAUDA-hFABP1-diclofenac ternary complex was verified with native MS. Docking predicted diclofenac binding in the portal region of FABP1 with DAUDA in the binding cavity. The catalytic rate constant of diclofenac hydroxylation by CYP2C9 was decreased by ∼50% (P < 0.01) in the presence of FABP1. Together, these results suggest that drugs form ternary complexes with hFABP1 and that hFABP1 binding in the liver will alter drug metabolism and clearance. SIGNIFICANCE STATEMENT: Many commonly prescribed drugs bind fatty acid binding protein 1 (FABP1), forming ternary complexes with FABP1 and the fluorescent fatty acid 11-(dansylamino)undecanoic acid. These findings suggest that drugs will bind to apo-FABP1 and fatty acid-bound FABP1 in the human liver. The high expression of FABP1 in the liver, together with drug binding to FABP1, may alter drug disposition processes in vivo.

CRISPR-Based KCC2 Upregulation Attenuates Drug-Resistant Seizure in Mouse Models of Epilepsy.

OBJECTIVE: The precise intervention of K-Cl cotransporter isoform 2 (KCC2) as a promising target for drug-resistant epilepsy remains elusive. METHODS: Here, we used a CRISPRa system delivered by adeno-associated viruses to specifically upregulate KCC2 in the subiculum to confirm its therapeutic potential in various in vivo epilepsy models. Calcium fiber photometry was used to reveal the role of KCC2 in the restoration of impaired GABAergic inhibition. RESULTS: CRISPRa system effectively upregulated KCC2 expression both in in vitro cell culture and in vivo brain region. Delivery of CRISPRa with adeno-associated viruses resulted in upregulating the subicular KCC2 level, contributing to alleviating the severity of hippocampal seizure and facilitating the anti-seizure effect of diazepam in a hippocampal kindling model. In a kainic acid-induced epilepticus status model, KCC2 upregulation greatly increased the termination percentage of diazepam-resistant epilepticus status with the broadened therapeutic window. More importantly, KCC2 upregulation attenuated valproate-resistant spontaneous seizure in a kainic acid-induced chronic epilepsy model. Finally, calcium fiber photometry showed CRISPRa-mediated KCC2 upregulation partially restored the impaired GABAA -mediated inhibition in epilepsy. INTERPRETATION: These results showed the translational potential of adeno-associated viruses-mediated delivery of CRISPRa for treating neurological disorders by modulating abnormal gene expression that is directly associated with neuronal excitability, validating KCC2 as a promising therapeutic target for treating drug-resistant epilepsy. ANN NEUROL 2023;94:91-105.

Recommendations on the Use of Multiple Labels in Human Mass Balance Studies.

The administration of radiolabeled drug candidates is considered the gold standard in absorption, distribution, metabolism, and excretion studies for small-molecule drugs since it allows facile and accurate quantification of parent drug, metabolites, and total drug-related material independent of the compound structure. The choice of the position of the radiolabel, typically 14C or 3H, is critical to obtain relevant information. Sometimes, a biotransformation reaction may lead to cleavage of a part of the molecule. As a result, only the radiolabeled portion can be followed, and information on the fate of the nonlabeled metabolite may be lost. Synthesis and administration of two or more radiolabeled versions of the parent drug as a mixture or in separate studies may resolve this issue but comes with additional challenges. In this paper, we address the questions that may be considered to help make the right choice whether to use a single or multiple radiolabel approach and discuss the pros and cons of different multiple-labeling strategies that can be taken as well as alternative methods that allow the nonlabeled part of the molecule to be followed. SIGNIFICANCE STATEMENT: Radiolabeled studies are the gold standard in drug metabolism research, but molecules can undergo cleavage with loss of the label. This often results in discussions around potential use of multiple labels, which seem to be occurring with increased frequency since an increasing proportion of the small-molecule drugs are tending towards larger molecular weights. This review provides insight and decision criteria in considering a multiple-label approach as well as pros and cons of different strategies that can be followed.

Comparison of Human Long-Term Liver Models for Clearance Prediction of Slowly Metabolized Compounds.

The accurate prediction of human clearance is an important task during drug development. The proportion of low clearance compounds has increased in drug development pipelines across the industry since such compounds may be dosed in lower amounts and at lower frequency. These type of compounds present new challenges to in vitro systems used for clearance extrapolation. In this study, we compared the accuracy of clearance predictions of suspension culture to four different long-term stable in vitro liver models, including HepaRG sandwich culture, the Hµrel stochastic co-culture, the Hepatopac micropatterned co-culture (MPCC), and a micro-array spheroid culture. Hepatocytes in long-term stable systems remained viable and active over several days of incubation. Although intrinsic clearance values were generally high in suspension culture, clearance of low turnover compounds could frequently not be determined using this method. Metabolic activity and intrinsic clearance values from HepaRG cultures were low and, consequently, many compounds with low turnover did not show significant decline despite long incubation times. Similarly, stochastic co-cultures occasionally failed to show significant turnover for multiple low and medium turnover compounds. Among the different methods, MPCCs and spheroids provided the most consistent measurements. Notably, all culture methods resulted in underprediction of clearance; this could, however, be compensated for by regression correction. Combined, the results indicate that spheroid culture as well as the MPCC system provide adequate in vitro tools for human extrapolation for compounds with low metabolic turnover. SIGNIFICANCE STATEMENT: In this study, we compared suspension cultures, HepaRG sandwich cultures, the Hµrel liver stochastic co-cultures, the Hepatopac micropatterned co-cultures (MPCC), and micro-array spheroid cultures for low clearance determination and prediction. Overall, HepaRG and suspension cultures showed modest value for the low determination and prediction of clearance compounds. The micro-array spheroid culture resulted in the most robust clearance measurements, whereas using the MPCC resulted in the most accurate prediction for low clearance compounds.