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Is Grocery Chain's FCA Win in the Bag?

Client Alert | 1 min read | 04.20.18

On March 22, 2018, an Indiana state trial court dismissed a qui tam action alleging that a large grocery chain knowingly failed to collect and remit state sales tax on hundreds of goods.  State of Indiana ex. rel Harmeyer v. The Kroger Co. et al. Under Indiana law, the state’s gross retail tax does not apply to “food and food ingredients,” but it does apply to candy, soft drinks, dietary supplements, and prepared foods.  Relator claimed 1,400 food items were mischaracterized as tax-exempt, for example, claiming protein bars are taxable candy rather than nontaxable food.

The judge held that the complaint failed to identify any particular false statement, noting also that the relator, whose similar case against a grocer in another state had been dismissed, was not an employee of the grocery chain, had no inside knowledge of what took place within the company, and merely presumed that the defendant’s characterization of the items as tax-exempt was false and done recklessly.  The court could not determine whether the 1,400 items constituted a substantial percentage of the products sold by defendant, and therefore, even if they were mischaracterized, the court could not presume recklessness from that number.  Last week, relator filed his appeal.

The dismissal is a blow to serial qui tam relators who, with no inside information, bring claims against companies based solely on a presumption that they must be non-compliant with an industry regulation.  The case also reflects an uptick in FCA litigation involving underpayment state taxes (previous post here). While the federal FCA bars tax-related actions (31 USC § 3729(d)), companies must still consider potential exposure in the jurisdictions that permit FCA suits arising from state tax matters.

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AI in Life Sciences: Ten Legal Considerations and Risks of AI Use in Drug Discovery and Development

Over the past several years, the biopharmaceutical industry has embraced artificial intelligence and machine learning (AI/ML) in near lockstep with the pace of AI/ML innovations. Today, industry leaders are using AI/ML to, among other things: discover and assess biological pathways, target chemical structures and sequences; design proteins; model pre-clinical and clinical trials; recruit and screen potential patient populations; evaluate clinical trial results and biomarker data; prepare regulatory filings; and manage supply chains. Deployment of new AI/ML models promises extraordinary advances in pharmaceutical development. However, as with any technological and scientific advances, the use of AI/ML also poses substantial legal risks that life sciences companies need to consider and proactively manage. ...