The Alzheimer’s Disease Drug Development Landscape

Background: Alzheimer’s disease (AD) is a devastating neurodegenerative disease leading to dementia. The field has made significant progress over the last 15 years. AD diagnosis has shifted from syndromal, based on signs and symptoms, to a biomarker construct based on the pathological hallmarks of the disease: amyloid β deposition, pathologic tau, and neurodegeneration. Numerous genetic risk factors for sporadic AD have been identified, providing further insight into the molecular underpinnings of the disease. For the last two decades, however, drug development for AD has been proven to be particularly challenging. Here, we provide a unique overview of the drug development landscape for AD. By comparing preclinical and clinical drug development pipelines, we aim to describe trends and differences regarding target classes and therapeutic modalities in preclinical and clinical development. Methods: We analyzed proprietary and public databases and company websites for drugs in preclinical development for AD by the pharmaceutical industry and major clinical trial registries for drugs in clinical development for AD. Drugs were categorized by target class and treatment modality. Results: We found a higher proportion of preclinical interventions targeting molecular pathways associated with sporadic AD genetic risk variants, compared to clinical stage interventions. These include apolipoprotein E (ApoE) and lipids, lysosomal/endosomal targets, and proteostasis. Further, we observed a trend suggesting that more traditional therapeutic modalities are developed for these novel targets, while more novel treatment modalities such as gene therapies and enzyme treatments are in development for more traditional targets such as amyloid β and tau. Interestingly, the percentage of amyloid β targeting therapies in preclinical development (19.2%) is even higher than the percentage in clinical development (10.7%), indicating that diversification away from interventions targeting amyloid-beta has not materialized. Inflammation is the second most popular target class in both preclinical and clinical development. Conclusions: Our observations show that the AD drug development pipeline is diversifying in terms of targets and treatment modalities, while amyloid-targeting therapies remain a prominent avenue of development as well. To further advance AD drug development, novel companion diagnostics are needed that are directed at disease mechanisms related to genetic risk factors of AD, both for patient stratification and assessment of therapeutic efficacy in clinical trials

Discovery and functional prioritization of Parkinson's disease candidate genes from large-scale whole exome sequencing.

BACKGROUND: Whole-exome sequencing (WES) has been successful in identifying genes that cause familial Parkinson's disease (PD). However, until now this approach has not been deployed to study large cohorts of unrelated participants. To discover rare PD susceptibility variants, we performed WES in 1148 unrelated cases and 503 control participants. Candidate genes were subsequently validated for functions relevant to PD based on parallel RNA-interference (RNAi) screens in human cell culture and Drosophila and C. elegans models. RESULTS: Assuming autosomal recessive inheritance, we identify 27 genes that have homozygous or compound heterozygous loss-of-function variants in PD cases. Definitive replication and confirmation of these findings were hindered by potential heterogeneity and by the rarity of the implicated alleles. We therefore looked for potential genetic interactions with established PD mechanisms. Following RNAi-mediated knockdown, 15 of the genes modulated mitochondrial dynamics in human neuronal cultures and four candidates enhanced α-synuclein-induced neurodegeneration in Drosophila. Based on complementary analyses in independent human datasets, five functionally validated genes-GPATCH2L, UHRF1BP1L, PTPRH, ARSB, and VPS13C-also showed evidence consistent with genetic replication. CONCLUSIONS: By integrating human genetic and functional evidence, we identify several PD susceptibility gene candidates for further investigation. Our approach highlights a powerful experimental strategy with broad applicability for future studies of disorders with complex genetic etiologies

The Alzheimer’s disease drug development landscape

Background: Alzheimer’s disease (AD) is a devastating neurodegenerative disease leading to dementia. The field has made significant progress over the last 15 years. AD diagnosis has shifted from syndromal, based on signs and symptoms, to a biomarker construct based on the pathological hallmarks of the disease: amyloid β deposition, pathologic tau, and neurodegeneration. Numerous genetic risk factors for sporadic AD have been identified, providing further insight into the molecular underpinnings of the disease. For the last two decades, however, drug development for AD has been proven to be particularly challenging. Here, we provide a unique overview of the drug development landscape for AD. By comparing preclinical and clinical drug development pipelines, we aim to describe trends and differences regarding target classes and therapeutic modalities in preclinical and clinical development. Methods: We analyzed proprietary and public databases and company websites for drugs in preclinical development for AD by the pharmaceutical industry and major clinical trial registries for drugs in clinical development for AD. Drugs were categorized by target class and treatment modality. Results: We found a higher proportion of preclinical interventions targeting molecular pathways associated with sporadic AD genetic risk variants, compared to clinical stage interventions. These include apolipoprotein E (ApoE) and lipids, lysosomal/endosomal targets, and proteostasis. Further, we observed a trend suggesting that more traditional therapeutic modalities are developed for these novel targets, while more novel treatment modalities such as gene therapies and enzyme treatments are in development for more traditional targets such as amyloid β and tau. Interestingly, the percentage of amyloid β targeting therapies in preclinical development (19.2%) is even higher than the percentage in clinical development (10.7%), indicating that diversification away from interventions targeting amyloid-beta has not materialized. Inflammation is the second most popular target class in both preclinical and clinical development. Conclusions: Our observations show that the AD drug development pipeline is diversifying in terms of targets and treatment modalities, while amyloid-targeting therapies remain a prominent avenue of development as well. To further advance AD drug development, novel companion diagnostics are needed that are directed at disease mechanisms related to genetic risk factors of AD, both for patient stratification and assessment of therapeutic efficacy in clinical trials

C9orf72 is differentially expressed in the central nervous system and myeloid cells and consistently reduced in C9orf72, MAPT and GRN mutation carriers

A non-coding hexanucleotide repeat expansion (HRE) in C9orf72 is a common cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) acting through a loss of function mechanism due to haploinsufficiency of C9orf72 or a gain of function mediated by aggregates of bidirectionally transcribed HRE-RNAs translated into di-peptide repeat (DPR) proteins. To fully understand regulation of C9orf72 expression we surveyed the C9orf72 locus using Cap Analysis of Gene Expression sequence data (CAGEseq). We observed C9orf72 was generally lowly expressed with the exception of a subset of myeloid cells, particularly CD14+ monocytes that showed up to seven fold higher expression as compared to central nervous system (CNS) and other tissues. The expression profile at the C9orf72 locus showed a complex architecture with differential expression of the transcription start sites (TSSs) for the annotated C9orf72 transcripts between myeloid and CNS tissues suggesting cell and/or tissue specific functions. We further detected novel TSSs in both the sense and antisense strand at the C9orf72 locus and confirmed their existence in brain tissues and CD14+ monocytes. Interestingly, our experiments showed a consistent decrease of C9orf72 coding transcripts not only in brain tissue and monocytes from C9orf72-HRE patients, but also in brains from MAPT and GRN mutation carriers together with an increase in antisense transcripts suggesting these could play a role in regulation of C9orf72. We found that the non-HRE related expression changes cannot be explained by promoter methylation but by the presence of the C9orf72-HRE risk haplotype and unknown functional interactions between C9orf72, MAPT and GRN. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40478-016-0306-7) contains supplementary material, which is available to authorized users

Unbiased cell-based screening in a neuronal cell model of Batten Disease highlights an interaction between Ca2+Homeostasis, autophagy, and CLN3 protein function

Abnormal accumulation of undigested macromolecules, often disease-specific, is a major feature of lysosomal and neurodegenerative disease and is frequently attributed to defective autophagy. The mechanistic underpinnings of the autophagy defects are the subject of intense research, which is aided by genetic disease models. To gain an improved understanding of the pathways regulating defective autophagy specifically in juvenile neuronal ceroid lipofuscinosis (JNCL or Batten disease), a neurodegenerative disease of childhood, we developed and piloted a GFP-microtubule-associated protein 1 light chain 3 (GFP-LC3) screening assay to identify, in an unbiased fashion, genotype-sensitive small molecule autophagy modifiers, employing a JNCL neuronal cell model bearing the most common disease mutation in CLN3. Thapsigargin, a sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) Ca2+ pump inhibitor, reproducibly displayed significantly more activity in the mouse JNCL cells, an effect that was also observed in human-induced pluripotent stem cell-derived JNCL neural progenitor cells. The mechanism of thapsigargin sensitivity was Ca2+-mediated, and autophagosome accumulation in JNCL cells could be reversed by Ca2+ chelation. Interrogation of intracellular Ca2+ handling highlighted alterations in endoplasmic reticulum, mitochondrial, and lysosomal Ca2+ pools and in store-operated Ca2+ uptake in JNCL cells. These results further support an important role for the CLN3 protein in intracellular Ca2+ handling and in autophagic pathway flux and establish a powerful new platform for therapeutic screening

Additional file 1: of C9orf72 is differentially expressed in the central nervous system and myeloid cells and consistently reduced in C9orf72, MAPT and GRN mutation carriers

Supplementary Data. Figure S1: Definition of TSSs at the C9orf72 locus; Figure S2: C9orf72 expression changes in challenged CD14+ monocytes. Figure S3: Distinct TSSs expression at C9orf72 locus in CNS, myeloid and lymphoid cells. Figure S4: Sense and antisense C9orf72 transcripts at the C9orf72 locus. Figure S5: Distinct C9orf72 TSSs expression in control brains. Figure S6: C9orf72 expression in adult and fetal cortex. Figure S7: C9orf72 expression level in CD14+ monocytes, brain tissue and microglia. Figure S8: C9orf72 expression in brains of patients with different neurodegenerative diseases. Figure S9: C9orf72 expression in CD14+ monocytes. Suppl. excel File 1: WGCNA results. Suppl. excel File 2: Correlations values between C9orf72 TSS(s) and all the other TSSs in the co-expressed modules. Table S1: Expression of C9orf72 TSSs as defined in CAGEseq dataset 1. Table S2: Expression of C9orf72 TSSs as defined in CAGEseq dataset 2. Table S3: List of primers used in this study. Table S4: Biological functions significant to the three modules related to C9orf72 TSSs as identified by WGCNA. Table S5: Biological functions significant for genes that correlate with C9orf72 TSSs in the WGCNA identified modules. Table S6: Summary of the Mann-Whitney test performed on NRQ values from qPCR experiments on medial frontal gyrus. (ZIP 20675ย�kb

Unbiased Cell-based Screening in a Neuronal Cell Model of Batten Disease Highlights an Interaction between Ca2+ Homeostasis, Autophagy, and CLN3 Protein Function

Abnormal accumulation of undigested macromolecules, often disease-specific, is a major feature of lysosomal and neurodegenerative disease and is frequently attributed to defective autophagy. The mechanistic underpinnings of the autophagy defects are the subject of intense research, which is aided by genetic disease models. To gain an improved understanding of the pathways regulating defective autophagy specifically in juvenile neuronal ceroid lipofuscinosis (JNCL or Batten disease), a neurodegenerative disease of childhood, we developed and piloted a GFP-microtubule-associated protein 1 light chain 3 (GFP-LC3) screening assay to identify, in an unbiased fashion, genotype-sensitive small molecule autophagy modifiers, employing a JNCL neuronal cell model bearing the most common disease mutation in CLN3. Thapsigargin, a sarco/endoplasmic reticulum Ca2+-ATPase (SERCA) Ca2+ pump inhibitor, reproducibly displayed significantly more activity in the mouse JNCL cells, an effect that was also observed in human-induced pluripotent stem cell-derived JNCL neural progenitor cells. The mechanism of thapsigargin sensitivity was Ca2+-mediated, and autophagosome accumulation in JNCL cells could be reversed by Ca2+ chelation. Interrogation of intracellular Ca2+ handling highlighted alterations in endoplasmic reticulum, mitochondrial, and lysosomal Ca2+ pools and in store-operated Ca2+ uptake in JNCL cells. These results further support an important role for the CLN3 protein in intracellular Ca2+ handling and in autophagic pathway flux and establish a powerful new platform for therapeutic screening

Additional file 1: of Discovery and functional prioritization of Parkinson’s disease candidate genes from large-scale whole exome sequencing

Includes all 12 additional tables with every table on a separate spreadsheet. The file format is an Excel spreadsheet. (XLSX 183 kb