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Obviousness-Type Double Patenting: Earlier Patented Genus Invalidates Later Species Claim

Client Alert | 1 min read | 07.27.07

A split Federal Circuit panel, in Astrazeneca AB et al v. KV Pharmaceutical Company et al. (No. 06-1254; July 23, 2007), affirms a district court’s judgment of invalidity based on obviousness-type double patenting. Astrazeneca et al. (“Astra”) had sued KV Pharmaceutical Company et al (“KV”), alleging that KV’s abbreviated new drug application (“ANDA”) seeking approval for an extended release metoprolol succinate product infringed U.S. Patent No. 5,081,154 (“the ‘154 patent”). The only claim of the ‘154 patent reads “Metoprolol succinate.” KV moved for summary judgment, alleging, inter alia, that the ‘154 patent was invalid based on obviousness-type double patenting over U.S. Patent No. 4,780,318 (“the ‘318 patent”).

The pertinent claim of the ‘318 patent, claim 8, was directed to a pharmaceutical composition having (A) a core comprising a therapeutically active compound selected from a group of eleven compounds, including metoprolol succinate, (B) a first inner layer coating, and (C) a second outer layer coating on the inner layer. Based upon claim 8, the district court granted KV’s double patenting-based summary judgment motion, finding that the compound of the ‘318 patent was a species of the genus claimed in the ‘054 patent.

In affirming the district court’s judgment , the Federal Circuit submits that “it would have been an obvious variation of claim 8 of the ‘318 patent to omit the inner layer (B) and the outer layer (C)” leaving only the metoprolol succinate. Judge Schall’s dissent states that he would have reversed the district court’s decision in part as “there is no double patenting simply because a later claimed element is set forth in an earlier claim to a combination.”

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Client Alert | 9 min read | 10.21.26

Artificial Intelligence in Life Sciences: Reliability of AI-Generated Evidence in Drug Discovery and Development

The life sciences sector is uniquely vulnerable to the inherent risks of AI-generated evidence in drug development and discovery. This client alert focuses on five core challenges: hallucinations, skewed predictions from unrepresentative training data, the opacity of AI reasoning, input sensitivity that undermines reproducibility, and adversarial manipulation of training data. To address these risks, companies should use experimental validation and human oversight as the common legal and regulatory safety net. This entails among other measures auditing AI outputs, characterizing training data, and structural oversight. Companies also should prepare for evolving frameworks and guidance from the U.S. Food and Drug Administration (FDA) and the European Union (EU) that address use of AI in drug discovery and development .  ...