Stenosis of the glomerulotubular neck in progressive chronic kidney disease.

BACKGROUND: Morphology and morphometric evaluation of lesions beyond conventional parameters can inform the pathophysiology of chronic kidney disease (CKD). We sought to determine whether the occurrence of glomerulotubular neck stenoses associates with progressive CKD. METHODS: We evaluated the normal parenchyma from radical nephrectomies removed for tumor between 2000 and 2021 and analyzed cortex for stenoses of the glomerulotubular neck. Stenosis of the glomerulotubular neck is defined a focal narrowing for which the draining tubule has a greater diameter than at the neck. Progressive CKD was defined as dialysis, kidney transplantation, sustained eGFR <10 ml/min per 1.73m2 or sustained 40% decline from the post-nephrectomy eGFR. Each case of progressive CKD was age-sex-matched to 2 controls without progressive CKD. Logistic regression models assessed the risk of progressive CKD with stenotic necks adjusting for other histological features, kidney function, and CKD risk factors. RESULTS: There were 65 cases with a mean of 255 glomeruli and 130 controls with a mean of 329 glomeruli. Among both cases and controls, 5% of glomeruli showed visible glomerulotubular necks. The proportion of necks that were stenotic was higher in cases than controls (35% vs. 11%, p<0.0001). Stenotic necks associated with progressive CKD independent of other histologic and clinical characteristics. CONCLUSION: Glomerulotubular neck stenosis is associated with development of progressive CKD.

Characterization of arteriosclerosis based on computer-aided measurements of intra-arterial thickness.

Purpose: Our purpose is to develop a computer vision approach to quantify intra-arterial thickness on digital pathology images of kidney biopsies as a computational biomarker of arteriosclerosis. Approach: The severity of the arteriosclerosis was scored (0 to 3) in 753 arteries from 33 trichrome-stained whole slide images (WSIs) of kidney biopsies, and the outer contours of the media, intima, and lumen were manually delineated by a renal pathologist. We then developed a multi-class deep learning (DL) framework for segmenting the different intra-arterial compartments (training dataset: 648 arteries from 24 WSIs; testing dataset: 105 arteries from 9 WSIs). Subsequently, we employed radial sampling and made measurements of media and intima thickness as a function of spatially encoded polar coordinates throughout the artery. Pathomic features were extracted from the measurements to collectively describe the arterial wall characteristics. The technique was first validated through numerical analysis of simulated arteries, with systematic deformations applied to study their effect on arterial thickness measurements. We then compared these computationally derived measurements with the pathologists' grading of arteriosclerosis. Results: Numerical validation shows that our measurement technique adeptly captured the decreasing smoothness in the intima and media thickness as the deformation increases in the simulated arteries. Intra-arterial DL segmentations of media, intima, and lumen achieved Dice scores of 0.84, 0.78, and 0.86, respectively. Several significant associations were identified between arteriosclerosis grade and pathomic features using our technique (e.g., intima-media ratio average [ τ = 0.52 , p < 0.0001 ]) through Kendall's tau analysis. Conclusions: We developed a computer vision approach to computationally characterize intra-arterial morphology on digital pathology images and demonstrate its feasibility as a potential computational biomarker of arteriosclerosis.

Paraneoplastic Cast Nephropathy Associated With Pancreatic Acinar Cell Carcinoma: A Kidney Biopsy Teaching Case.

Paraneoplastic cast nephropathy, a rare cause of acute kidney injury, is most commonly observed in cases of multiple myeloma and is characterized by the formation of intratubular casts composed of monoclonal light chains. Nonmonoclonal paraneoplastic cast nephropathy has also been reported in patients with pancreatic acinar cell carcinoma or prolactinoma. In this case report, we present a case of polyclonal cast nephropathy in a patient with metastatic acinar cell carcinoma. We aim to emphasize the significance of recognizing this uncommon complication in patients with solid tumors and to discuss the diagnostic challenges and potential pathophysiology of this unique condition.

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology.

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.

Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS.

Background: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis. Methods: Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). N=104 pathomic features were extracted from these segmented tubular substructures and summarized at the patient level using summary statistics. The tubular features were quantified across the biopsy and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA and non-IFTA in the NEPTUNE dataset. Minimum Redundancy Maximum Relevance was used in the NEPTUNE dataset to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset. Results: N=9 features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN datasets and increased prognostic accuracy for both outcomes (5.6%-7.7% and 1.6%-4.6% increase for disease progression and proteinuria remission, respectively) compared to conventional parameters alone in the NEPTUNE dataset. TBM thickness/area and TE simplification progressively increased from non- to pre- and mature IFTA. Conclusions: Previously under-recognized, quantifiable, and clinically relevant tubular features in the kidney parenchyma can enhance understanding of mechanisms of disease progression and risk stratification.

PatchSorter: a high throughput deep learning digital pathology tool for object labeling.

The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

ComPRePS: An Automated Cloud-based Image Analysis tool to democratize AI in Digital Pathology.

Artificial intelligence (AI) has extensive applications in a wide range of disciplines including healthcare and clinical practice. Advances in high-resolution whole-slide brightfield microscopy allow for the digitization of histologically stained tissue sections, producing gigapixel-scale whole-slide images (WSI). The significant improvement in computing and revolution of deep neural network (DNN)-based AI technologies over the last decade allow us to integrate massively parallelized computational power, cutting-edge AI algorithms, and big data storage, management, and processing. Applied to WSIs, AI has created opportunities for improved disease diagnostics and prognostics with the ultimate goal of enhancing precision medicine and resulting patient care. The National Institutes of Health (NIH) has recognized the importance of developing standardized principles for data management and discovery for the advancement of science and proposed the Findable, Accessible, Interoperable, Reusable, (FAIR) Data Principles1 with the goal of building a modernized biomedical data resource ecosystem to establish collaborative research communities. In line with this mission and to democratize AI-based image analysis in digital pathology, we propose ComPRePS: an end-to-end automated Computational Renal Pathology Suite which combines massive scalability, on-demand cloud computing, and an easy-to-use web-based user interface for data upload, storage, management, slide-level visualization, and domain expert interaction. Moreover, our platform is equipped with both in-house and collaborator developed sophisticated AI algorithms in the back-end server for image analysis to identify clinically relevant micro-anatomic functional tissue units (FTU) and to extract image features.

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine.

Many data resources generate, process, store, or provide kidney related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO), and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We have also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CZ CellxGene data resources and applied PMMO for integrative kidney data analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed under healthy control or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge integration in precision medicine.

Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Rationale and design of the Nephrotic Syndrome Study Network (NEPTUNE) Match in glomerular diseases: designing the right trial for the right patient, today.

Glomerular diseases are classified using a descriptive taxonomy that is not reflective of the heterogeneous underlying molecular drivers. This limits not only diagnostic and therapeutic patient management, but also impacts clinical trials evaluating targeted interventions. The Nephrotic Syndrome Study Network (NEPTUNE) is poised to address these challenges. The study has enrolled >850 pediatric and adult patients with proteinuric glomerular diseases who have contributed to deep clinical, histologic, genetic, and molecular profiles linked to long-term outcomes. The NEPTUNE Knowledge Network, comprising combined, multiscalar data sets, captures each participant's molecular disease processes at the time of kidney biopsy. In this editorial, we describe the design and implementation of NEPTUNE Match, which bridges a basic science discovery pipeline with targeted clinical trials. Noninvasive biomarkers have been developed for real-time pathway analyses. A Molecular Nephrology Board reviews the pathway maps together with clinical, laboratory, and histopathologic data assembled for each patient to compile a Match report that estimates the fit between the specific molecular disease pathway(s) identified in an individual patient and proposed clinical trials. The NEPTUNE Match report is communicated using established protocols to the patient and the attending nephrologist for use in their selection of available clinical trials. NEPTUNE Match represents the first application of precision medicine in nephrology with the aim of developing targeted therapies and providing the right medication for each patient with primary glomerular disease.

Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments.

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists' visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.

Relationships among Non-Neoplastic Histopathological Features, Kidney Function, Proteinuria, and Other Clinical Factors in Patients Undergoing Nephrectomy.

Introduction: The non-neoplastic kidney parenchyma from nephrectomies is often overlooked in routine examinations. We aimed to evaluate the associations between global glomerulosclerosis (GS), interstitial fibrosis (IF), or arteriosclerosis (AS) and estimated glomerular filtration rate (eGFR), dipstick proteinuria, and other clinical factors. Methods: We performed a cross-sectional analysis of 781 patients with nephrectomy. We used regression models with and without interaction factors. The tested exposures were GS, IF, or AS, and the outcome measures were GFR and dipstick proteinuria. Results: In multivariable analyses, increasing degrees of GS, IF, or AS were significantly associated with lower eGFR and proteinuria (p < 0.05 for each). Obesity and hypertension (HTN) modified the association between eGFR and degrees of GS, whereas proteinuria and cardiovascular disease (CVD) modified the association between eGFR and degrees of AS (p for interaction <0.05). Compared with GS <10%, GS >50% was associated with lower eGFR in patients with (-45 mL/min/1.73 m2) than without (-19 mL/min/1.73 m2) obesity, and GS >50% was associated with lower eGFR in patients with (-31 mL/min/1.73 m2) than without (-16 mL/min/1.73 m2) HTN. Compared with AS <26%, AS >50% was associated with lower eGFR in patients with (-11 mL/min/1.73 m2) than without (-6 mL/min/1.73 m2) proteinuria, and AS >50% was associated with lower eGFR in patients with (-23 mL/min/1.73 m2) than without (-7 mL/min/1.73 m2) CVD. Conclusion: Greater degrees of each GS, IF, and AS are independently associated with proteinuria and lower eGFR. Obesity, HTN, proteinuria, and CVD modify the relationship between eGFR and specific histopathological features of nephrosclerosis.

Tubular and Glomerular Size by Cortex Depth as Predictor of Progressive CKD after Radical Nephrectomy for Tumor.

SIGNIFICANCE STATEMENT: Glomerular size differs by cortex depth. Larger nephrons are prognostic of progressive kidney disease, but it is unknown whether this risk differs by cortex depth or by glomeruli versus proximal or distal tubule size. We studied the average minor axis diameter in oval proximal and distal tubules separately and by cortex depth in patients who had radical nephrectomy to remove a tumor from 2019 to 2020. In adjusted analyses, larger glomerular volume in the middle and deep cortex predicted progressive kidney disease. Wider proximal tubular diameter did not predict progressive kidney disease independent of glomerular volume. Wider distal tubular diameter showed a gradient of strength of prediction of progressive kidney disease in the more superficial cortex than in the deep cortex. BACKGROUND: Larger nephrons are prognostic of progressive kidney disease, but whether this risk differs by nephron segments or by depth in the cortex is unclear. METHODS: We studied patients who underwent radical nephrectomy for a tumor between 2000 and 2019. Large wedge kidney sections were scanned into digital images. We estimated the diameters of proximal and distal tubules by the minor axis of oval tubular profiles and estimated glomerular volume with the Weibel-Gomez stereological model. Analyses were performed separately in the superficial, middle, and deep cortex. Cox proportional hazard models assessed the risk of progressive CKD (dialysis, kidney transplantation, sustained eGFR <10 ml/min per 1.73 m 2 , or a sustained 40% decline from the postnephrectomy baseline eGFR) with glomerular volume or tubule diameters. At each cortical depth, models were unadjusted, adjusted for glomerular volume or tubular diameter, and further adjusted for clinical characteristics (age, sex, body mass index, hypertension, diabetes, postnephrectomy baseline eGFR, and proteinuria). RESULTS: Among 1367 patients were 62 progressive CKD events during a median follow-up of 4.5 years. Glomerular volume predicted CKD outcomes at all depths, but only in the middle and deep cortex after adjusted analyses. Proximal tubular diameter also predicted progressive CKD at any depth but not after adjusted analyses. Distal tubular diameter showed a gradient of more strongly predicting progressive CKD in the superficial than deep cortex, even in adjusted analysis. CONCLUSIONS: Larger glomeruli are independent predictors of progressive CKD in the deeper cortex, whereas in the superficial cortex, wider distal tubular diameters are an independent predictor of progressive CKD.

Defining the molecular correlate of arteriolar hyalinosis in kidney disease progression by integration of single cell transcriptomic analysis and pathology scoring.

Arteriolar hyalinosis in kidneys is an independent predictor of cardiovascular disease, the main cause of mortality in chronic kidney disease (CKD). The underlying molecular mechanisms of protein accumulation in the subendothelial space are not well understood. Using single cell transcriptomic data and whole slide images from kidney biopsies of patients with CKD and acute kidney injury in the Kidney Precision Medicine Project, the molecular signals associated with arteriolar hyalinosis were evaluated. Co-expression network analysis of the endothelial genes yielded three gene set modules as significantly associated with arteriolar hyalinosis. Pathway analysis of these modules showed enrichment of transforming growth factor beta / bone morphogenetic protein (TGFß / BMP) and vascular endothelial growth factor (VEGF) signaling pathways in the endothelial cell signatures. Ligand-receptor analysis identified multiple integrins and cell adhesion receptors as over-expressed in arteriolar hyalinosis, suggesting a potential role of integrin-mediated TGFß signaling. Further analysis of arteriolar hyalinosis associated endothelial module genes identified focal segmental glomerular sclerosis as an enriched term. On validation in gene expression profiles from the Nephrotic Syndrome Study Network cohort, one of the three modules was significantly associated with the composite endpoint (> 40% reduction in estimated glomerular filtration rate (eGFR) or kidney failure) independent of age, sex, race, and baseline eGFR, suggesting poor prognosis with elevated expression of genes in this module. Thus, integration of structural and single cell molecular features yielded biologically relevant gene sets, signaling pathways and ligand-receptor interactions, underlying arteriolar hyalinosis and putative targets for therapeutic intervention.

Progenitor hierarchy among parietal epithelial cells depicted at the single-cell level.

The role of parietal epithelial cells (PECs) in kidney function and disease was recently revisited. Building on previous studies of human kidney tissue, in the current issue, Liu et al. further characterize PECs using single-cell RNA sequencing data and confirm the crucial pathophysiological role of PECs in murine kidney biology as a reservoir for different types of progenitors.

Age-Based Versus Young-Adult Thresholds for Nephrosclerosis on Kidney Biopsy and Prognostic Implications for CKD.

SIGNIFICANCE STATEMENT: Nephrosclerosis (glomerulosclerosis, interstitial fibrosis, and tubular atrophy) is the defining pathology of both kidney aging and CKD. Optimal thresholds for nephrosclerosis that identify persons with a progressive disease are unknown. This study determined a young-age threshold (18-29 years) and age-based 95th percentile thresholds for nephrosclerosis on the basis of morphometry of kidney biopsy sections from normotensive living kidney donors. These thresholds were 7.1-fold to 36-fold higher in older (70 years or older) versus younger (aged 18-29 years) normotensive donors. Age-based thresholds, but not young-age threshold, were prognostic for determining risk of progressive CKD among patients who underwent a radical nephrectomy or a for-cause native kidney biopsy, suggesting that age-based thresholds are more useful than a single young-age threshold for identifying CKD on biopsy. BACKGROUND: Nephrosclerosis, defined by globally sclerotic glomeruli (GSG) and interstitial fibrosis and tubular atrophy (IFTA), is a pathology of both kidney aging and CKD. A comparison of risk of progressive CKD using aged-based thresholds for nephrosclerosis versus a single young-adult threshold is needed. METHODS: We conducted morphometric analyses of kidney biopsy images for %GSG, %IFTA, and IFTA foci density among 3020 living kidney donors, 1363 patients with kidney tumor, and 314 patients with native kidney disease. Using normotensive donors, we defined young-age thresholds (18-29 years) and age-based (roughly by decade) 95th percentile thresholds. We compared age-adjusted risk of progressive CKD (kidney failure or 40% decline in eGFR) between nephrosclerosis that was "normal compared with young," "normal for age but abnormal compared with young," and "abnormal for age" in patients with tumor and patients with kidney disease. RESULTS: The 95th percentiles in the youngest group (18-29 years) to the oldest group (70 years or older) ranged from 1.7% to 16% for %GSG, 0.18% to 6.5% for %IFTA, and 8.2 to 59.3 per cm 2 for IFTA foci density. Risk of progressive CKD did not differ between persons with nephrosclerosis "normal compared with young" versus "normal for age but abnormal compared with young." Risk of progressive CKD was significantly higher with %GSG, %IFTA, or IFTA foci density that was abnormal versus normal for age in both cohorts. CONCLUSIONS: Given that increased risk of progressive CKD occurs only when nephrosclerosis is abnormal for age, age-based thresholds for nephrosclerosis seem to be better than a single young-age threshold for identifying clinically relevant CKD.

Inaxaplin for Proteinuric Kidney Disease in Persons with Two APOL1 Variants.

BACKGROUND: Persons with toxic gain-of-function variants in the gene encoding apolipoprotein L1 (APOL1) are at greater risk for the development of rapidly progressive, proteinuric nephropathy. Despite the known genetic cause, therapies targeting proteinuric kidney disease in persons with two APOL1 variants (G1 or G2) are lacking. METHODS: We used tetracycline-inducible APOL1 human embryonic kidney (HEK293) cells to assess the ability of a small-molecule compound, inaxaplin, to inhibit APOL1 channel function. An APOL1 G2-homologous transgenic mouse model of proteinuric kidney disease was used to assess inaxaplin treatment for proteinuria. We then conducted a single-group, open-label, phase 2a clinical study in which inaxaplin was administered to participants who had two APOL1 variants, biopsy-proven focal segmental glomerulosclerosis, and proteinuria (urinary protein-to-creatinine ratio of ≥0.7 to <10 [with protein and creatinine both measured in grams] and an estimated glomerular filtration rate of ≥27 ml per minute per 1.73 m2 of body-surface area). Participants received inaxaplin daily for 13 weeks (15 mg for 2 weeks and 45 mg for 11 weeks) along with standard care. The primary outcome was the percent change from the baseline urinary protein-to-creatinine ratio at week 13 in participants who had at least 80% adherence to inaxaplin therapy. Safety was also assessed. RESULTS: In preclinical studies, inaxaplin selectively inhibited APOL1 channel function in vitro and reduced proteinuria in the mouse model. Sixteen participants were enrolled in the phase 2a study. Among the 13 participants who were treated with inaxaplin and met the adherence threshold, the mean change from the baseline urinary protein-to-creatinine ratio at week 13 was -47.6% (95% confidence interval, -60.0 to -31.3). In an analysis that included all the participants regardless of adherence to inaxaplin therapy, reductions similar to those in the primary analysis were observed in all but 1 participant. Adverse events were mild or moderate in severity; none led to study discontinuation. CONCLUSIONS: Targeted inhibition of APOL1 channel function with inaxaplin reduced proteinuria in participants with two APOL1 variants and focal segmental glomerulosclerosis. (Funded by Vertex Pharmaceuticals; VX19-147-101 ClinicalTrials.gov number, NCT04340362.).