Pharmacokinetic (PK) parameters are critical to drug development, but many remain embedded in free-text biomedical literature rather than a structured form. Existing extraction approaches primarily target explicit parameter mentions, while a substantial proportion of PK values must be inferred from context. We introduce implicit PK parameter extraction, where parameter identity is not explicitly stated but must be resolved through domain-grounded, multi-sentence reasoning. To address this challenge, we propose CORVA, a validation-enhanced LLM-based extraction framework that integrates reasoning and domain constraints to extract structured PK tuples from text. We also construct a labelled dataset from DrugBank absorption descriptions, of which 47.5% involve implicit expressions. Relative to the strongest baseline, the proposed method improves F1 by 35.3% and tuple accuracy by 52.9%. CORVA achieves the highest implicit recognition rate at 45.8%, which is 10.5 points ahead of the runner-up. Our results demonstrate that reliable implicit PK extraction requires validation as a first-class component rather than a post-processing step, which offers a practical framework for scalable knowledge extraction from biomedical literature. The code and data are available at https://github.com/wuqinhao/CORVA.