Federal Trade Commission Actions on Prescription Drugs, 2000-2022.

Importance: The Federal Trade Commission's (FTC) oversight role in the pharmaceutical market is critical to the health of patients and the health care system. This study characterized the FTC's policy on the pharmaceutical market in recent decades, identifying the types of actions it has favored, barriers it has faced, and authorities that remain untested. Objective: To review FTC legal actions in the pharmaceutical market from 2000-2022. Evidence Review: Legal actions were determined through manual review of search results from the FTC's online Legal Library as well as a 2023 FTC report on pharmaceutical actions. The alleged misconduct, type of legal action taken, timing, and outcome were collected from press releases, complaints, orders, and other legal documents. Findings: From 2000-2022, the FTC challenged 62 mergers, brought 22 enforcement actions against allegedly unlawful business practices, and made 1 rule related to pharmaceuticals. Alleged misconduct in enforcement actions involved anticompetitive settlements in patent litigation (n = 11), unilateral actions by brand manufacturers to delay generic competition (n = 6), noncompete agreements (n = 4), and monopolization (n = 3), with 10 outcomes involving monetary payment, totaling $1.6 billion. Of the 62 mergers the FTC challenged, 61 were allowed to continue, 58 after divesting certain drugs to third-party competitors. The FTC's reliance on drug divestitures decreased from 18 drugs per year from 2000-2017 to 4.3 per year from 2017-2023. Conclusions and Relevance: The FTC brought about 1 enforcement action and 3 merger actions per year against pharmaceutical manufacturers from 2000-2022, pursuing a small fraction of the estimated misconduct and consolidation in the pharmaceutical marketplace. Although the FTC faces substantial legal and practical limitations, important tools remain untested, including a rule defining "unfair methods of competition," that may allow it to more effectively prevent repetitive patterns of anticompetitive behavior.

Isolation of a Novel Pythium Species, P. thermoculicivorax, and Trichoderma sp. from Natural Enzootic Mosquito Larval Infections.

Only a handful of microbial mosquito larval pathogens have been described to date. Sampling several natural enzootic infections of mosquito larvae in southwestern Florida indicated the presence of microbial pathogens capable of extensive larval mortality. A microscopic analysis of one sample site revealed extensive apparent growth of a Pythium-like microbe on mosquito larvae, with the highest degree of infection observed in the siphon and head regions. Structures consistent with sporangia were seen on infected insects after lactophenol blue staining, and higher-resolution scanning electron microscopy (SEM) micrographs showed sporangia and encysted zoospores targeting the head and siphon regions. The isolate was single-colony purified, and molecular identification targeting the ITS and COX1 loci coupled to phylogenetic reconstruction indicated that the isolate belonged to the Pythium genus but was distinct from its closest characterized species, P. inflatum. Morphological features were characterized, with the isolate showing rapid growth on all mycological media tested and relatively high thermotolerance, capable of robust growth at 37 °C; hence, it was designated P. thermoculicivorax. Sampling from a second series of natural infections of mosquito larvae resulted in the molecular identification of three Trichoderma isolates, one with high similarity to T. strigosum and the other two clustering closely with T. asperellum. These data highlight the occurrence of natural enzootic infections of mosquito larvae, potentially as a resource for the identification of new mosquito pathogens.

Federal Enforcement of Pharmaceutical Fraud under the False Claims Act, 2006-2022.

CONTEXT: The False Claims Act is the US federal government's primary tool for identifying and penalizing pharmaceutical fraud. The Department of Justice uses the False Claims Act to bring civil cases against drug manufacturers that allegedly obtain improper payment from federal programs. METHODS: The authors searched the Department of Justice website for press releases published between 2006 and 2022 that announced fraud actions brought against drug companies. They then used the World Health Organization's Anatomical Therapeutic Classification index to identify the classes of prescription drugs implicated in fraud actions. FINDINGS: During fiscal years 2006-2022, payments by six manufacturers amounted to more than 28% of total payments made as a result of federal False Claims Act actions. Nervous system and cardiovascular drugs were the classes of medications most commonly implicated in alleged fraud. Federal officials most frequently alleged that companies improperly promoted nervous system drugs and paid kickbacks to increase revenues from cardiovascular, antineoplastic and immunomodulating, and alimentary tract and metabolism drugs. CONCLUSIONS: Despite frequent pharmaceutical fraud settlements and penalties, incidence of alleged fraud among drug companies remains high. Alternative methods for preventing and deterring fraud could help safeguard our health systems and promote public health, and policy makers should ensure that effective fraud enforcement complements preventive public health regulation.

Inequitable Conduct and Invalidation of Patents Related to Food and Drug Administration-Regulated Products.

This study examines the frequency of drug patent invalidations based on inequitable conduct.

Protecting Medicare's Discretion to Say No to Unproven Therapies.

In this Viewpoint, Kesselheim and coauthors discuss 2 bills in Congress that would curtail Medicare's ability to decline, limit, or conditionally cover medical products that lack robust evidence and argue that officials should distinguish between better and worse therapies when determining reimbursement.

Authority of Medicare to Limit Coverage of FDA-Approved Products: Legal and Policy Considerations.

Importance: When the US Food and Drug Administration (FDA) approves a drug or medical device on the basis of limited clinical evidence, the Centers for Medicare & Medicaid Services (CMS) must decide whether the therapy is "reasonable and necessary" for coverage among Medicare beneficiaries. However, the legal underpinnings of CMS's authority to shape coverage of FDA-regulated products under Medicare Part B are controversial. To clarify this area, we reviewed relevant legal precedents on CMS's approaches to limit coverage and recent decisions Medicare has issued affecting coverage for FDA-regulated products. Observations: The CMS continues to exercise considerable legal discretion to limit coverage of FDA-authorized products to only uses it determines are reasonable and necessary for patients with Medicare. Courts have upheld this discretion repeatedly, emphasizing the difference between Medicare's coverage criteria and the FDA's review standards. As more new drugs and devices come to market without solid evidence of efficacy on clinical outcomes, or have narrow benefit-risk considerations, CMS may increasingly rely on forms of limited or conditional coverage, including coverage with evidence development (CED), which provides reimbursement only in the context of a clinical trial or registry. Conclusions and Relevance: The ability of CMS to condition or limit coverage of FDA-approved products is a commonsense necessity for this crucial taxpayer-funded program. Although courts have thus far deferred to the authority of CMS to make such decisions on the basis of its clear statutory discretion and public health expertise, Congress may want to act to reaffirm statutory language giving CMS sufficient flexibility to craft coverage determinations that reflect the evidence for a product's use.

Association of Advisory Committee Votes With US Food and Drug Administration Decision-Making on Prescription Drugs, 2010-2021.

Importance: The US Food and Drug Administration (FDA) often relies on independent advisory committees when making decisions about the approval of prescription drugs or their withdrawal from the market. These committees provide the FDA with valuable insight and an opportunity to build public trust through transparent deliberation, but recent controversies have raised questions about the optimal use of FDA advisory committees. Objective: To assess the frequency, purposes, and voting outcomes of human drug advisory committees convened from 2010 to 2021 and the FDA's corresponding actions. Design, Setting, and Participants: This qualitative study used a manual review of meeting summaries prepared by FDA staff for the 18 human drug advisory committees active at any time from January 1, 2010, to December 31, 2021, as well as FDA announcements and press releases, drug labels and approval data, industry publications, and company press releases. Main Outcomes and Measures: Outcomes of votes on regulatory questions were recorded using meeting minutes. Alignment of FDA action with advisory votes for new drugs and indications was judged as of 1 year after the vote was held and as of November 30, 2022. Results: The FDA held 409 human drug advisory committee meetings from 2010 to 2021. Committees were convened less frequently over time, from a high of 50 in 2012 to a low of 18 in 2020 and 2021. Much of this decrease occurred at committee meetings involving votes on initial approvals, which declined from a high of 26 in 2012 to a low of 8 in 2021. Overall, FDA regulatory actions aligned with 262 of 298 advisory committee votes on initial approvals, supplemental approvals, withdrawals of approval, and safety actions (88%). Approval followed 142 of 147 positive votes for initial approvals (97%) and 33 of 36 positive votes for supplemental indications (92%), while nonapproval followed 40 of 60 negative votes on initial approvals (67%) and 18 of 21 negative votes on supplemental indications (86%). Conclusions and Relevance: In this qualitative study, there was consistent alignment between advisory votes and FDA action across years and subject areas, but the number of meetings decreased over time. Discordance between FDA actions and advisory committee votes was most frequently an approval after a negative vote. This study demonstrated that these committees have played a key role in the FDA's decision-making process but that the FDA sought independent expert advice less frequently over time even as it continued to follow it. The role of advisory committees in the current regulatory landscape should be more clearly and publicly defined.

Host switching by an ambrosia beetle fungal mutualist: Mycangial colonization of indigenous beetles by the invasive laurel wilt fungal pathogen.

Ambrosia beetles require their fungal symbiotic partner as their cultivated (farmed) food source in tree galleries. While most fungal-beetle partners do not kill the host trees they inhabit, since their introduction (invasion) into the United states around ~2002, the invasive beetle Xyleborus glabratus has vectored its mutualist partner (but plant pathogenic) fungus, Harringtonia lauricola, resulting in the deaths of over 300 million trees. Concerningly, indigenous beetles have been caught bearing H. lauricola. Here, we show colonization of the mycangia of the indigenous X. affinis ambrosia beetle by H. lauricola. Mycangial colonization occurred within 1 h of feeding, with similar levels seen for H. lauricola as found for the native X. affinis-R. arxii fungal partner. Fungal mycangial occupancy was stable over time and after removal of the fungal source, but showed rapid turnover when additional fungal cells were available. Microscopic visualization revealed two pre-oral mycangial pouches of ~100-200 × 25-50 µm/each, with narrow entry channels of 25-50 × 3-10 µm. Fungi within the mycangia underwent a dimorphic transition from filamentous/blastospore growth to yeast-like budding with alterations to membrane structures. These data identify the characteristics of ambrosia beetle mycangial colonization, implicating turnover as a mechanism for host switching of H. lauricola to other ambrosia beetle species.

Holding Pharmaceutical and Medical Device Executives Accountable as Responsible Corporate Officers.

Importance: When a drug or medical device company violates federal law, the government has a powerful tool to use: the Park doctrine. The aim of the doctrine is to protect patients from the harms of an unsafe or fraudulent medical marketplace by targeting the executives who run the companies that make revenues on these products while violating federal law, rather than have that risk borne by patients or impersonal corporate entities; however, public reports of drug and device company executives being prosecuted in Park doctrine cases are uncommon. Objective: To identify Park prosecutions and characterize their role in US Department of Justice (DOJ) enforcement efforts related to misconduct in the drug and medical device industry. Evidence Review: A systematic search of Westlaw, Google Scholar, DOJ press releases, and the legal literature was conducted. Findings: Thirteen cases of executives from 6 drug and medical device companies prosecuted under the Park doctrine since 2000 were identified. The prosecutions resulted in 11 guilty pleas and 2 jury trials, leading to 2 convictions. Of the 6 companies, 3 were drug manufacturers, 2 were medical device manufacturers, and 1 was a compounding pharmacy. All 3 drug manufacturers were opioid manufacturers, of which 2 executives were charged for unlawful promotion, and 1 was charged for manufacturing errors. Both device manufacturer executives were charged with unlawful promotion. All but 3 prosecutions alleged the defendants' complicity or personal involvement in the misconduct, which Park does not require. By contrast, most large settlements with the DOJ over alleged misconduct in the past 2 decades did not result in individual liability for executives. Conclusions and Relevance: These findings suggest that the government has not exercised the full scope of its authority to prosecute corporate officials responsible for the illegal behavior of the drug and device companies they run. Enforcement under a reinvigorated Park doctrine could better promote the doctrine's goal of protecting patients.

Immigration Law, Public Health, and the Future of Public Charge Policymaking.

U.S. immigration law has excluded noncitizens likely to become a "public charge" since 1882. When the Trump administration proposed a new Rule expanding the interpretation of that exclusion in 2018, over 55,000 people wrote public comments. These comments, overwhelmingly opposed to the change, are the subject of Rachel Fabi and Lauren Zahn's insightful article in this issue of The Journal of Law, Medicine, and Ethics. The themes they identify resonate with the history of the public charge exclusion, which has always reflected a tension between two aims of American governance - to provide for those in need of assistance, and to shape the nation's citizenry according to ideals of self-sufficiency.

Unwanted Advice? Frequency, Characteristics, And Outcomes Of Negative Advisory Committee Votes For FDA-Approved Drugs.

Substantial controversy arose in 2021 when the Food and Drug Administration (FDA) approved the Alzheimer's disease drug aducanumab (Aduhelm) under its accelerated approval program despite a nearly unanimous negative advisory committee vote. Advisory committees are convened before some FDA decisions to provide insight for the agency's decision-making process. To understand the frequency, characteristics, and outcomes of cases in which the FDA authorizes a drug against the recommendations of an advisory committee, we reviewed all FDA advisory committee referrals for new drugs approved during the period 2010-21. The fraction of approved drugs that had been referred to an advisory committee decreased from 55 percent to 6 percent annually, with FDA approvals of negatively reviewed drugs occurring about once a year. Qualitative analysis of committee meetings revealed variability in the substance and wording of key voting questions. Reforms such as transparent criteria for when a drug should be subject to external scrutiny, consistent wording of voting questions, and clear regulatory responses to negative recommendations can better ensure public confidence in the FDA approval process.

A Question-and-Answer System to Extract Data From Free-Text Oncological Pathology Reports (CancerBERT Network): Development Study.

BACKGROUND: Information in pathology reports is critical for cancer care. Natural language processing (NLP) systems used to extract information from pathology reports are often narrow in scope or require extensive tuning. Consequently, there is growing interest in automated deep learning approaches. A powerful new NLP algorithm, bidirectional encoder representations from transformers (BERT), was published in late 2018. BERT set new performance standards on tasks as diverse as question answering, named entity recognition, speech recognition, and more. OBJECTIVE: The aim of this study is to develop a BERT-based system to automatically extract detailed tumor site and histology information from free-text oncological pathology reports. METHODS: We pursued three specific aims: extract accurate tumor site and histology descriptions from free-text pathology reports, accommodate the diverse terminology used to indicate the same pathology, and provide accurate standardized tumor site and histology codes for use by downstream applications. We first trained a base language model to comprehend the technical language in pathology reports. This involved unsupervised learning on a training corpus of 275,605 electronic pathology reports from 164,531 unique patients that included 121 million words. Next, we trained a question-and-answer (Q&A) model that connects a Q&A layer to the base pathology language model to answer pathology questions. Our Q&A system was designed to search for the answers to two predefined questions in each pathology report: What organ contains the tumor? and What is the kind of tumor or carcinoma? This involved supervised training on 8197 pathology reports, each with ground truth answers to these 2 questions determined by certified tumor registrars. The data set included 214 tumor sites and 193 histologies. The tumor site and histology phrases extracted by the Q&A model were used to predict International Classification of Diseases for Oncology, Third Edition (ICD-O-3), site and histology codes. This involved fine-tuning two additional BERT models: one to predict site codes and another to predict histology codes. Our final system includes a network of 3 BERT-based models. We call this CancerBERT network (caBERTnet). We evaluated caBERTnet using a sequestered test data set of 2050 pathology reports with ground truth answers determined by certified tumor registrars. RESULTS: caBERTnet's accuracies for predicting group-level site and histology codes were 93.53% (1895/2026) and 97.6% (1993/2042), respectively. The top 5 accuracies for predicting fine-grained ICD-O-3 site and histology codes with 5 or more samples each in the training data set were 92.95% (1794/1930) and 96.01% (1853/1930), respectively. CONCLUSIONS: We have developed an NLP system that outperforms existing algorithms at predicting ICD-O-3 codes across an extensive range of tumor sites and histologies. Our new system could help reduce treatment delays, increase enrollment in clinical trials of new therapies, and improve patient outcomes.

Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients.

Automatic brain tumor segmentation is particularly challenging on magnetic resonance imaging (MRI) with marked pathologies, such as brain tumors, which usually cause large displacement, abnormal appearance, and deformation of brain tissue. Despite an abundance of previous literature on learning-based methodologies for MRI segmentation, few works have focused on tackling MRI skull stripping of brain tumor patient data. This gap in literature can be associated with the lack of publicly available data (due to concerns about patient identification) and the labor-intensive nature of generating ground truth labels for model training. In this retrospective study, we assessed the performance of Dense-Vnet in skull stripping brain tumor patient MRI trained on our large multi-institutional brain tumor patient dataset. Our data included pretreatment MRI of 668 patients from our in-house institutional review board-approved multi-institutional brain tumor repository. Because of the absence of ground truth, we used imperfect automatically generated training labels using SPM12 software. We trained the network using common MRI sequences in oncology: T1-weighted with gadolinium contrast, T2-weighted fluid-attenuated inversion recovery, or both. We measured model performance against 30 independent brain tumor test cases with available manual brain masks. All images were harmonized for voxel spacing and volumetric dimensions before model training. Model training was performed using the modularly structured deep learning platform NiftyNet that is tailored toward simplifying medical image analysis. Our proposed approach showed the success of a weakly supervised deep learning approach in MRI brain extraction even in the presence of pathology. Our best model achieved an average Dice score, sensitivity, and specificity of, respectively, 94.5, 96.4, and 98.5% on the multi-institutional independent brain tumor test set. To further contextualize our results within existing literature on healthy brain segmentation, we tested the model against healthy subjects from the benchmark LBPA40 dataset. For this dataset, the model achieved an average Dice score, sensitivity, and specificity of 96.2, 96.6, and 99.2%, which are, although comparable to other publications, slightly lower than the performance of models trained on healthy patients. We associate this drop in performance with the use of brain tumor data for model training and its influence on brain appearance.

Fungal mutualisms and pathosystems: life and death in the ambrosia beetle mycangia.

Ambrosia beetles and their microbial communities, housed in specialized structures termed mycangia, represent one of the oldest and most diverse systems of mutualism and parasitism described thus far. Comprised of core filamentous fungal members, but also including bacteria and yeasts, the mycangia represent a unique adaptation that allows beetles to store and transport their source of nutrition. Although perhaps the most ancient of "farmers," the nature of these interactions remains largely understudied, with the exception of a handful of emerging pathosystems, where the fungal partner acts as a potentially devastating tree pathogen. Such virulence is often seen during "invasions," where (invasive) beetles carrying the fungal symbiont/plant pathogen expand into new territories and presumably "naïve" trees. Here, we summarize recent findings on the phylogenetic relationships between beetles and their symbionts and advances in the developmental and genetic characterization of the mechanisms that underlie insect-fungal-plant interactions. Results on genomic, transcriptomic, and metabolomic aspects of these relationships are described. Although many members of the fungal Raffaelea-beetle symbiont genera are relatively harmless to host trees, specialized pathosystems including wilt diseases of laurel and oak, caused by specific subspecies (R. lauricola and R. quercus, in the USA and East Asia, respectively), have emerged as potent plant pathogens capable of killing healthy trees. With the development of genetic tools, coupled to biochemical and microscopic techniques, the ambrosia beetle-fungal symbiont is establishing itself as a unique model system to study the molecular determinants and mechanisms that underlie the convergences of symbioses, mutualism, parasitism, and virulence. KEY POINTS: • Fungal-beetle symbioses are diverse and ancient examples of microbial farming. • The mycangium is a specialized structure on insects that houses microbial symbionts. • Some beetle symbiotic fungi are potent plant pathogens vectored by the insect.

Unique Attributes of the Laurel Wilt Fungal Pathogen, Raffaelea lauricola, as Revealed by Metabolic Profiling.

Raffaelea lauricola is the causative agent of laurel wilt, a devastating disease of lauraceous trees. R. lauricola is also an obligate nutritional symbiont of several ambrosia beetle species who act as vectors for the pathogen. Here, we sought to establish the baseline "phenome" of R. lauricola with knowledge concerning its metabolic capability, expanding our understanding of how these processes are impacted by environmental and host nutrients. Phenotypic screening using a microarray of over one thousand compounds was used to generate a detailed profile of R. lauricola substrate utilization and chemical sensitivity. These data revealed (i) relatively restricted carbon utilization, (ii) broad sulfur and phosphate utilization, and (iii) pH and osmotic sensitivities that could be rescued by specific compounds. Additional growth profiling on fatty acids revealed toxicity on C10 substrates and lower, with robust growth on C12-C18 fatty acids. Conditions for lipid droplet (LD) visualization and LD dynamics were examined using a series of lipid dyes. These data provide unique insights regarding R. lauricola metabolism and physiology, and identify distinct patterns of substrate usage and sensitivity which likely reflect important aspects of the host-microbe interface and can be exploited for the development of strategies for mitigating the spread of laurel wilt.

Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

Purpose: Deep learning (DL) algorithms have shown promising results for brain tumor segmentation in MRI. However, validation is required prior to routine clinical use. We report the first randomized and blinded comparison of DL and trained technician segmentations. Approach: We compiled a multi-institutional database of 741 pretreatment MRI exams. Each contained a postcontrast T1-weighted exam, a T2-weighted fluid-attenuated inversion recovery exam, and at least one technician-derived tumor segmentation. The database included 729 unique patients (470 males and 259 females). Of these exams, 641 were used for training the DL system, and 100 were reserved for testing. We developed a platform to enable qualitative, blinded, controlled assessment of lesion segmentations made by technicians and the DL method. On this platform, 20 neuroradiologists performed 400 side-by-side comparisons of segmentations on 100 test cases. They scored each segmentation between 0 (poor) and 10 (perfect). Agreement between segmentations from technicians and the DL method was also evaluated quantitatively using the Dice coefficient, which produces values between 0 (no overlap) and 1 (perfect overlap). Results: The neuroradiologists gave technician and DL segmentations mean scores of 6.97 and 7.31, respectively ( p < 0.00007 ). The DL method achieved a mean Dice coefficient of 0.87 on the test cases. Conclusions: This was the first objective comparison of automated and human segmentation using a blinded controlled assessment study. Our DL system learned to outperform its "human teachers" and produced output that was better, on average, than its training data.

High efficiency transformation and mutant screening of the laurel wilt pathogen, Raffaelea lauricola.

The fungal pathogen, Raffaelea lauricola, is the causative agent of laurel wilt, a devastating disease affecting the Lauraceae family. The fungus is vectored by ambrosia beetles that carry the fungus in specialized structures (mycangia), with the fungus acting as a symbiont and food source for beetle larvae growing in tree galleries. In order to probe the molecular basis for plant pathogenicity and insect symbiosis of the laurel wilt fungus, molecular tools including establishment of efficient transformation protocols are required. Resistance marker profiling revealed susceptibility of R. lauricola to phosphinothricin, chlorimuron ethyl, hygromycin, and benomyl. Agrobacterium-mediated transformation using either the bar or sur marker resulted in 1-200 transformants/105 spores. A second protocol using lithium acetate-polyethylene glycol (LiAc-PEG) treatment of fungal blastospores yielded 5-60 transformants/µg DNA/108 cells. Transformants were mitotically stable (at least 5 generations), and > 95% of transformants showed a single integration event. R. lauricola strains expressing green and red fluorescent proteins (EGFP and RFP), as well as glucuronidase (GUS), were constructed. Using the Agrobacterium-mediated method, a random T-DNA insertion library was constructed, and genetic screens led to the isolation of developmental mutants as well as mutants displaying enhanced resistance to sodium dodecyl sulfate (SDS) or fluconazole, and those showing decreased susceptibility to biphenol. These results establish simple and reliable genetic tools for transformation of R. lauricola needed for genetic dissection of the symbiotic and virulent lifestyles exhibited by this fungus and establish a library of insertion mutants that can be used in various genetic screens to dissect molecular pathways. KEY POINTS: • Vectors and transformation protocols were developed for Raffaelea lauricola. • Method was used for construction of a random insertion mutant library. • Mutant library was validated by phenotypic screens for resistance and susceptibility to various agents.