Product Images Lovastatin

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Product Label Images

The following 5 images provide visual information about the product associated with Lovastatin NDC 43063-983 by Pd-rx Pharmaceuticals, Inc., such as packaging, labeling, and the appearance of the drug itself. This resource could be helpful for medical professionals, pharmacists, and patients seeking to verify medication information and ensure they have the correct product.

43063983 Label - 43063983

43063983 Label - 43063983

This is a description of Lovastatin 20 mg USP tablets manufactured by PHARMACEUTICALS. The tablets are sold in a bottle containing 30 tablets. The LOT numbers and their expiry dates are specified as LOT 119D22 with an expiry date of 10/2021. The product code is NDC 43063-983-30. The label contains a warning to contact a doctor in case of side effects and to report them to the FDA. The instructions for use are mentioned as taking a certain number of tablets per day. The text also contains a barcode, GTIN number, and SNO number.*

Structural Formula - lov00 0004 01

Structural Formula - lov00 0004 01

Table I - lov00 0004 02

Table I - lov00 0004 02

Figure 1 - lov00 0004 03

Figure 1 - lov00 0004 03

This is a graphical representation (Figure 1) showing the proportion of participants who did not experience an acute major coronary event (primary endpoint) over a follow-up period of up to four years. There is no additional information available.*

Table VII - lov00 0005 04

Table VII - lov00 0005 04

The text provides a list of drug interactions that are associated with an increased risk of myopathy or rhabdomyolysis, a possible side effect of certain medications. The drugs mentioned include strong CYP3A4 inhibitors, HIV protease inhibitors, several other medications, and products containing cobicistat. The prescribing recommendations suggest avoiding lovastatin when taking certain medications or when consuming grapefruit juice. The dosage of lovastatin is limited to 20 mg or 40 mg daily for some medications.*

* The product label images have been analyzed using a combination of traditional computing and machine learning techniques. It should be noted that the descriptions provided may not be entirely accurate as they are experimental in nature. Use the information in this page at your own discretion and risk.